Unnamed: 0 int64 0 41k | title stringlengths 4 274 | category stringlengths 5 18 | summary stringlengths 22 3.66k | theme stringclasses 8
values |
|---|---|---|---|---|
37,809 | Geometrical Interpretation of Shannon's Entropy Based on the Born Rule | cs.IT | In this paper we will analyze discrete probability distributions in which
probabilities of particular outcomes of some experiment (microstates) can be
represented by the ratio of natural numbers (in other words, probabilities are
represented by digital numbers of finite representation length). We will
introduce several... | computer science |
37,810 | Statistical mechanics of neocortical interactions: Portfolio of
Physiological Indicators | cs.CE | There are several kinds of non-invasive imaging methods that are used to
collect data from the brain, e.g., EEG, MEG, PET, SPECT, fMRI, etc. It is
difficult to get resolution of information processing using any one of these
methods. Approaches to integrate data sources may help to get better resolution
of data and bett... | computer science |
37,811 | Simulating Spiking Neural P systems without delays using GPUs | cs.DC | We present in this paper our work regarding simulating a type of P system
known as a spiking neural P system (SNP system) using graphics processing units
(GPUs). GPUs, because of their architectural optimization for parallel
computations, are well-suited for highly parallelizable problems. Due to the
advent of general ... | computer science |
37,812 | A hybrid neuro--wavelet predictor for QoS control and stability | cs.NE | For distributed systems to properly react to peaks of requests, their
adaptation activities would benefit from the estimation of the amount of
requests. This paper proposes a solution to produce a short-term forecast based
on data characterising user behaviour of online services. We use \emph{wavelet
analysis}, providi... | computer science |
37,813 | Using MOEAs To Outperform Stock Benchmarks In The Presence of Typical
Investment Constraints | cs.CE | Portfolio managers are typically constrained by turnover limits, minimum and
maximum stock positions, cardinality, a target market capitalization and
sometimes the need to hew to a style (such as growth or value). In addition,
portfolio managers often use multifactor stock models to choose stocks based
upon their respe... | computer science |
37,814 | Robust Mission Design Through Evidence Theory and Multi-Agent
Collaborative Search | cs.CE | In this paper, the preliminary design of a space mission is approached
introducing uncertainties on the design parameters and formulating the
resulting reliable design problem as a multiobjective optimization problem.
Uncertainties are modelled through evidence theory and the belief, or
credibility, in the successful a... | computer science |
37,815 | Approximated Computation of Belief Functions for Robust Design
Optimization | cs.CE | This paper presents some ideas to reduce the computational cost of
evidence-based robust design optimization. Evidence Theory crystallizes both
the aleatory and epistemic uncertainties in the design parameters, providing
two quantitative measures, Belief and Plausibility, of the credibility of the
computed value of the... | computer science |
37,816 | EURETILE 2010-2012 summary: first three years of activity of the
European Reference Tiled Experiment | cs.DC | This is the summary of first three years of activity of the EURETILE FP7
project 247846. EURETILE investigates and implements brain-inspired and
fault-tolerant foundational innovations to the system architecture of massively
parallel tiled computer architectures and the corresponding programming
paradigm. The execution... | computer science |
37,817 | Universal Memcomputing Machines | cs.NE | We introduce the notion of universal memcomputing machines (UMMs): a class of
brain-inspired general-purpose computing machines based on systems with memory,
whereby processing and storing of information occur on the same physical
location. We analytically prove that the memory properties of UMMs endow them
with univer... | computer science |
37,818 | EURETILE D7.3 - Dynamic DAL benchmark coding, measurements on MPI
version of DPSNN-STDP (distributed plastic spiking neural net) and
improvements to other DAL codes | cs.DC | The EURETILE project required the selection and coding of a set of dedicated
benchmarks. The project is about the software and hardware architecture of
future many-tile distributed fault-tolerant systems. We focus on dynamic
workloads characterised by heavy numerical processing requirements. The
ambition is to identify... | computer science |
37,819 | Parallel Graph Partitioning for Complex Networks | cs.DC | Processing large complex networks like social networks or web graphs has
recently attracted considerable interest. In order to do this in parallel, we
need to partition them into pieces of about equal size. Unfortunately, previous
parallel graph partitioners originally developed for more regular mesh-like
networks do n... | computer science |
37,820 | Time Resolution Dependence of Information Measures for Spiking Neurons:
Atoms, Scaling, and Universality | cs.NE | The mutual information between stimulus and spike-train response is commonly
used to monitor neural coding efficiency, but neuronal computation broadly
conceived requires more refined and targeted information measures of
input-output joint processes. A first step towards that larger goal is to
develop information measu... | computer science |
37,821 | When slower is faster | nlin.AO | The slower is faster (SIF) effect occurs when a system performs worse as its
components try to do better. Thus, a moderate individual efficiency actually
leads to a better systemic performance. The SIF effect takes place in a variety
of phenomena. We review studies and examples of the SIF effect in pedestrian
dynamics,... | computer science |
37,822 | Recursive Sparse Point Process Regression with Application to
Spectrotemporal Receptive Field Plasticity Analysis | cs.NE | We consider the problem of estimating the sparse time-varying parameter
vectors of a point process model in an online fashion, where the observations
and inputs respectively consist of binary and continuous time series. We
construct a novel objective function by incorporating a forgetting factor
mechanism into the poin... | computer science |
37,823 | Evolutionary Algorithms: Concepts, Designs, and Applications in
Bioinformatics: Evolutionary Algorithms for Bioinformatics | cs.NE | Since genetic algorithm was proposed by John Holland (Holland J. H., 1975) in
the early 1970s, the study of evolutionary algorithm has emerged as a popular
research field (Civicioglu & Besdok, 2013). Researchers from various scientific
and engineering disciplines have been digging into this field, exploring the
unique ... | computer science |
37,824 | Multi-objective Active Control Policy Design for Commensurate and
Incommensurate Fractional Order Chaotic Financial Systems | math.OC | In this paper, an active control policy design for a fractional order (FO)
financial system is attempted, considering multiple conflicting objectives. An
active control template as a nonlinear state feedback mechanism is developed
and the controller gains are chosen within a multi-objective optimization (MOO)
framework... | computer science |
37,825 | Information-theoretic interpretation of tuning curves for multiple
motion directions | cs.IT | We have developed an efficient information-maximization method for computing
the optimal shapes of tuning curves of sensory neurons by optimizing the
parameters of the underlying feedforward network model. When applied to the
problem of population coding of visual motion with multiple directions, our
method yields seve... | computer science |
37,826 | Learning Criticality in an Embodied Boltzmann Machine | nlin.AO | Many biological and cognitive systems do not operate deep into one or other
regime of activity. Instead, they exploit critical surfaces poised at
transitions in their parameter space. The pervasiveness of criticality in
natural systems suggests that there may be general principles inducing this
behaviour. However, ther... | computer science |
37,827 | Criticality as It Could Be: organizational invariance as self-organized
criticality in embodied agents | nlin.AO | This paper outlines a methodological approach for designing adaptive agents
driving themselves near points of criticality. Using a synthetic approach we
construct a conceptual model that, instead of specifying mechanistic
requirements to generate criticality, exploits the maintenance of an
organizational structure capa... | computer science |
37,828 | Adaptation to criticality through organizational invariance in embodied
agents | nlin.AO | Many biological and cognitive systems do not operate deep within one or other
regime of activity. Instead, they are poised at critical points located at
transitions of their parameter space. The pervasiveness of criticality suggests
that there may be general principles inducing this behaviour, yet there is no
well-foun... | computer science |
37,829 | Testing Optimality of Sequential Decision-Making | cs.IT | This paper provides a statistical method to test whether a system that
performs a binary sequential hypothesis test is optimal in the sense of
minimizing the average decision times while taking decisions with given
reliabilities. The proposed method requires samples of the decision times, the
decision outcomes, and the... | computer science |
37,830 | Separating populations with wide data: A spectral analysis | stat.ML | In this paper, we consider the problem of partitioning a small data sample
drawn from a mixture of $k$ product distributions. We are interested in the
case that individual features are of low average quality $\gamma$, and we want
to use as few of them as possible to correctly partition the sample. We analyze
a spectral... | computer science |
37,831 | Learning from dependent observations | stat.ML | In most papers establishing consistency for learning algorithms it is assumed
that the observations used for training are realizations of an i.i.d. process.
In this paper we go far beyond this classical framework by showing that support
vector machines (SVMs) essentially only require that the data-generating
process sa... | computer science |
37,832 | Kernels and Ensembles: Perspectives on Statistical Learning | stat.ME | Since their emergence in the 1990's, the support vector machine and the
AdaBoost algorithm have spawned a wave of research in statistical machine
learning. Much of this new research falls into one of two broad categories:
kernel methods and ensemble methods. In this expository article, I discuss the
main ideas behind t... | computer science |
37,833 | Locality and low-dimensions in the prediction of natural experience from
fMRI | stat.ML | Functional Magnetic Resonance Imaging (fMRI) provides dynamical access into
the complex functioning of the human brain, detailing the hemodynamic activity
of thousands of voxels during hundreds of sequential time points. One approach
towards illuminating the connection between fMRI and cognitive function is
through dec... | computer science |
37,834 | An Approximation Ratio for Biclustering | cs.DS | The problem of biclustering consists of the simultaneous clustering of rows
and columns of a matrix such that each of the submatrices induced by a pair of
row and column clusters is as uniform as possible. In this paper we approximate
the optimal biclustering by applying one-way clustering algorithms
independently on t... | computer science |
37,835 | Recursive Bias Estimation and $L_2$ Boosting | stat.ME | This paper presents a general iterative bias correction procedure for
regression smoothers. This bias reduction schema is shown to correspond
operationally to the $L_2$ Boosting algorithm and provides a new statistical
interpretation for $L_2$ Boosting. We analyze the behavior of the Boosting
algorithm applied to commo... | computer science |
37,836 | Least angle and $\ell_1$ penalized regression: A review | stat.ME | Least Angle Regression is a promising technique for variable selection
applications, offering a nice alternative to stepwise regression. It provides
an explanation for the similar behavior of LASSO ($\ell_1$-penalized
regression) and forward stagewise regression, and provides a fast
implementation of both. The idea has... | computer science |
37,837 | On central tendency and dispersion measures for intervals and hypercubes | stat.CO | The uncertainty or the variability of the data may be treated by considering,
rather than a single value for each data, the interval of values in which it
may fall. This paper studies the derivation of basic description statistics for
interval-valued datasets. We propose a geometrical approach in the
determination of s... | computer science |
37,838 | A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in
Machine Learning | stat.ML | We extend the well-known BFGS quasi-Newton method and its memory-limited
variant LBFGS to the optimization of nonsmooth convex objectives. This is done
in a rigorous fashion by generalizing three components of BFGS to
subdifferentials: the local quadratic model, the identification of a descent
direction, and the Wolfe ... | computer science |
37,839 | Gaussian Processes and Limiting Linear Models | stat.ME | Gaussian processes retain the linear model either as a special case, or in
the limit. We show how this relationship can be exploited when the data are at
least partially linear. However from the perspective of the Bayesian posterior,
the Gaussian processes which encode the linear model either have probability of
nearly... | computer science |
37,840 | Symmetry in Data Mining and Analysis: A Unifying View based on Hierarchy | stat.ML | Data analysis and data mining are concerned with unsupervised pattern finding
and structure determination in data sets. The data sets themselves are
explicitly linked as a form of representation to an observational or otherwise
empirical domain of interest. "Structure" has long been understood as symmetry
which can tak... | computer science |
37,841 | Predicting Regional Classification of Levantine Ivory Sculptures: A
Machine Learning Approach | stat.ML | Art historians and archaeologists have long grappled with the regional
classification of ancient Near Eastern ivory carvings. Based on the visual
similarity of sculptures, individuals within these fields have proposed object
assemblages linked to hypothesized regional production centers. Using
quantitative rather than ... | computer science |
37,842 | Data spectroscopy: Eigenspaces of convolution operators and clustering | stat.ML | This paper focuses on obtaining clustering information about a distribution
from its i.i.d. samples. We develop theoretical results to understand and use
clustering information contained in the eigenvectors of data adjacency matrices
based on a radial kernel function with a sufficiently fast tail decay. In
particular, ... | computer science |
37,843 | From Data to the p-Adic or Ultrametric Model | stat.ML | We model anomaly and change in data by embedding the data in an ultrametric
space. Taking our initial data as cross-tabulation counts (or other input data
formats), Correspondence Analysis allows us to endow the information space with
a Euclidean metric. We then model anomaly or change by an induced ultrametric.
The in... | computer science |
37,844 | Survival tree and meld to predict long term survival in liver
transplantation waiting list | stat.ML | Background: Many authors have described MELD as a predictor of short-term
mortality in the liver transplantation waiting list. However MELD score
accuracy to predict long term mortality has not been statistically evaluated.
Objective: The aim of this study is to analyze the MELD score as well as other
variables as a pr... | computer science |
37,845 | Non-linear regression models for Approximate Bayesian Computation | stat.CO | Approximate Bayesian inference on the basis of summary statistics is
well-suited to complex problems for which the likelihood is either
mathematically or computationally intractable. However the methods that use
rejection suffer from the curse of dimensionality when the number of summary
statistics is increased. Here w... | computer science |
37,846 | An Information Geometric Framework for Dimensionality Reduction | stat.ML | This report concerns the problem of dimensionality reduction through
information geometric methods on statistical manifolds. While there has been
considerable work recently presented regarding dimensionality reduction for the
purposes of learning tasks such as classification, clustering, and
visualization, these method... | computer science |
37,847 | Statistical ranking and combinatorial Hodge theory | stat.ML | We propose a number of techniques for obtaining a global ranking from data
that may be incomplete and imbalanced -- characteristics almost universal to
modern datasets coming from e-commerce and internet applications. We are
primarily interested in score or rating-based cardinal data. From raw ranking
data, we construc... | computer science |
37,848 | P-values for high-dimensional regression | stat.ME | Assigning significance in high-dimensional regression is challenging. Most
computationally efficient selection algorithms cannot guard against inclusion
of noise variables. Asymptotically valid p-values are not available. An
exception is a recent proposal by Wasserman and Roeder (2008) which splits the
data into two pa... | computer science |
37,849 | Penalized Orthogonal-Components Regression for Large p Small n Data | stat.ME | We propose a penalized orthogonal-components regression (POCRE) for large p
small n data. Orthogonal components are sequentially constructed to maximize,
upon standardization, their correlation to the response residuals. A new
penalization framework, implemented via empirical Bayes thresholding, is
presented to effecti... | computer science |
37,850 | A D.C. Programming Approach to the Sparse Generalized Eigenvalue Problem | stat.ML | In this paper, we consider the sparse eigenvalue problem wherein the goal is
to obtain a sparse solution to the generalized eigenvalue problem. We achieve
this by constraining the cardinality of the solution to the generalized
eigenvalue problem and obtain sparse principal component analysis (PCA), sparse
canonical cor... | computer science |
37,851 | Maximum Entropy Discrimination Markov Networks | stat.ML | In this paper, we present a novel and general framework called {\it Maximum
Entropy Discrimination Markov Networks} (MaxEnDNet), which integrates the
max-margin structured learning and Bayesian-style estimation and combines and
extends their merits. Major innovations of this model include: 1) It
generalizes the extant ... | computer science |
37,852 | Sparse partial least squares for on-line variable selection in
multivariate data streams | stat.ML | In this paper we propose a computationally efficient algorithm for on-line
variable selection in multivariate regression problems involving high
dimensional data streams. The algorithm recursively extracts all the latent
factors of a partial least squares solution and selects the most important
variables for each facto... | computer science |
37,853 | Ultrametric Wavelet Regression of Multivariate Time Series: Application
to Colombian Conflict Analysis | stat.ML | We first pursue the study of how hierarchy provides a well-adapted tool for
the analysis of change. Then, using a time sequence-constrained hierarchical
clustering, we develop the practical aspects of a new approach to wavelet
regression. This provides a new way to link hierarchical relationships in a
multivariate time... | computer science |
37,854 | Context tree selection and linguistic rhythm retrieval from written
texts | stat.ML | The starting point of this article is the question "How to retrieve
fingerprints of rhythm in written texts?" We address this problem in the case
of Brazilian and European Portuguese. These two dialects of Modern Portuguese
share the same lexicon and most of the sentences they produce are superficially
identical. Yet t... | computer science |
37,855 | Dual Augmented Lagrangian Method for Efficient Sparse Reconstruction | stat.ML | We propose an efficient algorithm for sparse signal reconstruction problems.
The proposed algorithm is an augmented Lagrangian method based on the dual
sparse reconstruction problem. It is efficient when the number of unknown
variables is much larger than the number of observations because of the dual
formulation. More... | computer science |
37,856 | Bayesian MAP Model Selection of Chain Event Graphs | stat.ME | The class of chain event graph models is a generalisation of the class of
discrete Bayesian networks, retaining most of the structural advantages of the
Bayesian network for model interrogation, propagation and learning, while more
naturally encoding asymmetric state spaces and the order in which events
happen. In this... | computer science |
37,857 | Percolation Thresholds of Updated Posteriors for Tracking Causal Markov
Processes in Complex Networks | stat.ML | Percolation on complex networks has been used to study computer viruses,
epidemics, and other casual processes. Here, we present conditions for the
existence of a network specific, observation dependent, phase transition in the
updated posterior of node states resulting from actively monitoring the
network. Since tradi... | computer science |
37,858 | Discrete Temporal Models of Social Networks | stat.ML | We propose a family of statistical models for social network evolution over
time, which represents an extension of Exponential Random Graph Models (ERGMs).
Many of the methods for ERGMs are readily adapted for these models, including
maximum likelihood estimation algorithms. We discuss models of this type and
their pro... | computer science |
37,859 | Online EM Algorithm for Hidden Markov Models | stat.CO | Online (also called "recursive" or "adaptive") estimation of fixed model
parameters in hidden Markov models is a topic of much interest in times series
modelling. In this work, we propose an online parameter estimation algorithm
that combines two key ideas. The first one, which is deeply rooted in the
Expectation-Maxim... | computer science |
37,860 | Sparse Canonical Correlation Analysis | stat.ML | We present a novel method for solving Canonical Correlation Analysis (CCA) in
a sparse convex framework using a least squares approach. The presented method
focuses on the scenario when one is interested in (or limited to) a primal
representation for the first view while having a dual representation for the
second view... | computer science |
37,861 | Bayesian orthogonal component analysis for sparse representation | stat.ME | This paper addresses the problem of identifying a lower dimensional space
where observed data can be sparsely represented. This under-complete dictionary
learning task can be formulated as a blind separation problem of sparse sources
linearly mixed with an unknown orthogonal mixing matrix. This issue is
formulated in a... | computer science |
37,862 | Slow Learners are Fast | math.OC | Online learning algorithms have impressive convergence properties when it
comes to risk minimization and convex games on very large problems. However,
they are inherently sequential in their design which prevents them from taking
advantage of modern multi-core architectures. In this paper we prove that
online learning ... | computer science |
37,863 | An Iterative Algorithm for Fitting Nonconvex Penalized Generalized
Linear Models with Grouped Predictors | stat.ML | High-dimensional data pose challenges in statistical learning and modeling.
Sometimes the predictors can be naturally grouped where pursuing the
between-group sparsity is desired. Collinearity may occur in real-world
high-dimensional applications where the popular $l_1$ technique suffers from
both selection inconsisten... | computer science |
37,864 | On the numeric stability of the SFA implementation sfa-tk | stat.ML | Slow feature analysis (SFA) is a method for extracting slowly varying
features from a quickly varying multidimensional signal. An open source
Matlab-implementation sfa-tk makes SFA easily useable. We show here that under
certain circumstances, namely when the covariance matrix of the nonlinearly
expanded data does not ... | computer science |
37,865 | MedLDA: A General Framework of Maximum Margin Supervised Topic Models | stat.ML | Supervised topic models utilize document's side information for discovering
predictive low dimensional representations of documents. Existing models apply
the likelihood-based estimation. In this paper, we present a general framework
of max-margin supervised topic models for both continuous and categorical
response var... | computer science |
37,866 | High-dimensional variable selection for Cox's proportional hazards model | stat.ML | Variable selection in high dimensional space has challenged many contemporary
statistical problems from many frontiers of scientific disciplines. Recent
technology advance has made it possible to collect a huge amount of covariate
information such as microarray, proteomic and SNP data via bioimaging
technology while ob... | computer science |
37,867 | Asymptotic risks of Viterbi segmentation | math.PR | We consider the maximum likelihood (Viterbi) alignment of a hidden Markov
model (HMM). In an HMM, the underlying Markov chain is usually hidden and the
Viterbi alignment is often used as the estimate of it. This approach will be
referred to as the Viterbi segmentation. The goodness of the Viterbi
segmentation can be me... | computer science |
37,868 | The Dynamic ECME Algorithm | stat.CO | The ECME algorithm has proven to be an effective way of accelerating the EM
algorithm for many problems. Recognising the limitation of using prefixed
acceleration subspace in ECME, we propose the new Dynamic ECME (DECME)
algorithm which allows the acceleration subspace to be chosen dynamically. Our
investigation of an ... | computer science |
37,869 | Algebraic Comparison of Partial Lists in Bioinformatics | stat.ML | The outcome of a functional genomics pipeline is usually a partial list of
genomic features, ranked by their relevance in modelling biological phenotype
in terms of a classification or regression model. Due to resampling protocols
or just within a meta-analysis comparison, instead of one list it is often the
case that ... | computer science |
37,870 | Quantum learning: optimal classification of qubit states | stat.ML | Pattern recognition is a central topic in Learning Theory with numerous
applications such as voice and text recognition, image analysis, computer
diagnosis. The statistical set-up in classification is the following: we are
given an i.i.d. training set $(X_{1},Y_{1}),... (X_{n},Y_{n})$ where $X_{i}$
represents a feature... | computer science |
37,871 | Reconstruction of Causal Networks by Set Covering | cs.DS | We present a method for the reconstruction of networks, based on the order of
nodes visited by a stochastic branching process. Our algorithm reconstructs a
network of minimal size that ensures consistency with the data. Crucially, we
show that global consistency with the data can be achieved through purely local
consid... | computer science |
37,872 | Slice sampling covariance hyperparameters of latent Gaussian models | stat.CO | The Gaussian process (GP) is a popular way to specify dependencies between
random variables in a probabilistic model. In the Bayesian framework the
covariance structure can be specified using unknown hyperparameters.
Integrating over these hyperparameters considers different possible
explanations for the data when maki... | computer science |
37,873 | Tree-Structured Stick Breaking Processes for Hierarchical Data | stat.ME | Many data are naturally modeled by an unobserved hierarchical structure. In
this paper we propose a flexible nonparametric prior over unknown data
hierarchies. The approach uses nested stick-breaking processes to allow for
trees of unbounded width and depth, where data can live at any node and are
infinitely exchangeab... | computer science |
37,874 | Distributed Algorithms for Learning and Cognitive Medium Access with
Logarithmic Regret | cs.NI | The problem of distributed learning and channel access is considered in a
cognitive network with multiple secondary users. The availability statistics of
the channels are initially unknown to the secondary users and are estimated
using sensing decisions. There is no explicit information exchange or prior
agreement amon... | computer science |
37,875 | Graph-Valued Regression | stat.ML | Undirected graphical models encode in a graph $G$ the dependency structure of
a random vector $Y$. In many applications, it is of interest to model $Y$ given
another random vector $X$ as input. We refer to the problem of estimating the
graph $G(x)$ of $Y$ conditioned on $X=x$ as ``graph-valued regression.'' In
this pap... | computer science |
37,876 | Stochastic Search with an Observable State Variable | math.OC | In this paper we study convex stochastic search problems where a noisy
objective function value is observed after a decision is made. There are many
stochastic search problems whose behavior depends on an exogenous state
variable which affects the shape of the objective function. Currently, there is
no general purpose ... | computer science |
37,877 | Optimizing an Organized Modularity Measure for Topographic Graph
Clustering: a Deterministic Annealing Approach | stat.ML | This paper proposes an organized generalization of Newman and Girvan's
modularity measure for graph clustering. Optimized via a deterministic
annealing scheme, this measure produces topologically ordered graph clusterings
that lead to faithful and readable graph representations based on clustering
induced graphs. Topog... | computer science |
37,878 | Probabilistic Models over Ordered Partitions with Application in
Learning to Rank | cs.IR | This paper addresses the general problem of modelling and learning rank data
with ties. We propose a probabilistic generative model, that models the process
as permutations over partitions. This results in super-exponential
combinatorial state space with unknown numbers of partitions and unknown
ordering among them. We... | computer science |
37,879 | The Loss Rank Criterion for Variable Selection in Linear Regression
Analysis | stat.ME | Lasso and other regularization procedures are attractive methods for variable
selection, subject to a proper choice of shrinkage parameter. Given a set of
potential subsets produced by a regularization algorithm, a consistent model
selection criterion is proposed to select the best one among this preselected
set. The a... | computer science |
37,880 | Model Selection by Loss Rank for Classification and Unsupervised
Learning | stat.ME | Hutter (2007) recently introduced the loss rank principle (LoRP) as a
generalpurpose principle for model selection. The LoRP enjoys many attractive
properties and deserves further investigations. The LoRP has been well-studied
for regression framework in Hutter and Tran (2010). In this paper, we study the
LoRP for clas... | computer science |
37,881 | Variational approximation for heteroscedastic linear models and matching
pursuit algorithms | stat.ME | Modern statistical applications involving large data sets have focused
attention on statistical methodologies which are both efficient computationally
and able to deal with the screening of large numbers of different candidate
models. Here we consider computationally efficient variational Bayes approaches
to inference ... | computer science |
37,882 | Evolutionary distances in the twilight zone -- a rational kernel
approach | stat.ML | Phylogenetic tree reconstruction is traditionally based on multiple sequence
alignments (MSAs) and heavily depends on the validity of this information
bottleneck. With increasing sequence divergence, the quality of MSAs decays
quickly. Alignment-free methods, on the other hand, are based on abstract
string comparisons ... | computer science |
37,883 | A ROAD to Classification in High Dimensional Space | stat.ML | For high-dimensional classification, it is well known that naively performing
the Fisher discriminant rule leads to poor results due to diverging spectra and
noise accumulation. Therefore, researchers proposed independence rules to
circumvent the diverse spectra, and sparse independence rules to mitigate the
issue of n... | computer science |
37,884 | Regularized Least-Mean-Square Algorithms | stat.ME | We consider adaptive system identification problems with convex constraints
and propose a family of regularized Least-Mean-Square (LMS) algorithms. We show
that with a properly selected regularization parameter the regularized LMS
provably dominates its conventional counterpart in terms of mean square
deviations. We es... | computer science |
37,885 | Toward a Classification of Finite Partial-Monitoring Games | cs.GT | Partial-monitoring games constitute a mathematical framework for sequential
decision making problems with imperfect feedback: The learner repeatedly
chooses an action, opponent responds with an outcome, and then the learner
suffers a loss and receives a feedback signal, both of which are fixed
functions of the action a... | computer science |
37,886 | How the result of graph clustering methods depends on the construction
of the graph | stat.ML | We study the scenario of graph-based clustering algorithms such as spectral
clustering. Given a set of data points, one first has to construct a graph on
the data points and then apply a graph clustering algorithm to find a suitable
partition of the graph. Our main question is if and how the construction of the
graph (... | computer science |
37,887 | A Generalized Least Squares Matrix Decomposition | stat.ME | Variables in many massive high-dimensional data sets are structured, arising
for example from measurements on a regular grid as in imaging and time series
or from spatial-temporal measurements as in climate studies. Classical
multivariate techniques ignore these structural relationships often resulting
in poor performa... | computer science |
37,888 | Submodular meets Spectral: Greedy Algorithms for Subset Selection,
Sparse Approximation and Dictionary Selection | stat.ML | We study the problem of selecting a subset of k random variables from a large
set, in order to obtain the best linear prediction of another variable of
interest. This problem can be viewed in the context of both feature selection
and sparse approximation. We analyze the performance of widely used greedy
heuristics, usi... | computer science |
37,889 | Semi-supervised logistic discrimination for functional data | stat.ME | Multi-class classification methods based on both labeled and unlabeled
functional data sets are discussed. We present a semi-supervised logistic model
for classification in the context of functional data analysis. Unknown
parameters in our proposed model are estimated by regularization with the help
of EM algorithm. A ... | computer science |
37,890 | Fast Inference of Interactions in Assemblies of Stochastic
Integrate-and-Fire Neurons from Spike Recordings | stat.ML | We present two Bayesian procedures to infer the interactions and external
currents in an assembly of stochastic integrate-and-fire neurons from the
recording of their spiking activity. The first procedure is based on the exact
calculation of the most likely time courses of the neuron membrane potentials
conditioned by ... | computer science |
37,891 | A Kernel Approach to Tractable Bayesian Nonparametrics | stat.ML | Inference in popular nonparametric Bayesian models typically relies on
sampling or other approximations. This paper presents a general methodology for
constructing novel tractable nonparametric Bayesian methods by applying the
kernel trick to inference in a parametric Bayesian model. For example, Gaussian
process regre... | computer science |
37,892 | Constrained Mixture Models for Asset Returns Modelling | stat.ML | The estimation of asset return distributions is crucial for determining
optimal trading strategies. In this paper we describe the constrained mixture
model, based on a mixture of Gamma and Gaussian distributions, to provide an
accurate description of price trends as being clearly positive, negative or
ranging while acc... | computer science |
37,893 | Randomized Smoothing for Stochastic Optimization | math.OC | We analyze convergence rates of stochastic optimization procedures for
non-smooth convex optimization problems. By combining randomized smoothing
techniques with accelerated gradient methods, we obtain convergence rates of
stochastic optimization procedures, both in expectation and with high
probability, that have opti... | computer science |
37,894 | Metamodel-based importance sampling for structural reliability analysis | stat.ME | Structural reliability methods aim at computing the probability of failure of
systems with respect to some prescribed performance functions. In modern
engineering such functions usually resort to running an expensive-to-evaluate
computational model (e.g. a finite element model). In this respect simulation
methods, whic... | computer science |
37,895 | Variational Bayes approach for model aggregation in unsupervised
classification with Markovian dependency | stat.ML | We consider a binary unsupervised classification problem where each
observation is associated with an unobserved label that we want to retrieve.
More precisely, we assume that there are two groups of observation: normal and
abnormal. The `normal' observations are coming from a known distribution
whereas the distributio... | computer science |
37,896 | MissForest - nonparametric missing value imputation for mixed-type data | stat.AP | Modern data acquisition based on high-throughput technology is often facing
the problem of missing data. Algorithms commonly used in the analysis of such
large-scale data often depend on a complete set. Missing value imputation
offers a solution to this problem. However, the majority of available
imputation methods are... | computer science |
37,897 | Order-preserving factor analysis (OPFA) | stat.ML | We present a novel factor analysis method that can be applied to the
discovery of common factors shared among trajectories in multivariate time
series data. These factors satisfy a precedence-ordering property: certain
factors are recruited only after some other factors are activated.
Precedence-ordering arise in appli... | computer science |
37,898 | Spectrum Sensing for Cognitive Radio Using Kernel-Based Learning | cs.NI | Kernel method is a very powerful tool in machine learning. The trick of
kernel has been effectively and extensively applied in many areas of machine
learning, such as support vector machine (SVM) and kernel principal component
analysis (kernel PCA). Kernel trick is to define a kernel function which relies
on the inner-... | computer science |
37,899 | Ergodic Mirror Descent | math.OC | We generalize stochastic subgradient descent methods to situations in which
we do not receive independent samples from the distribution over which we
optimize, but instead receive samples that are coupled over time. We show that
as long as the source of randomness is suitably ergodic---it converges quickly
enough to a ... | computer science |
37,900 | Multidimensional Scaling in the Poincare Disk | stat.ML | Multidimensional scaling (MDS) is a class of projective algorithms
traditionally used in Euclidean space to produce two- or three-dimensional
visualizations of datasets of multidimensional points or point distances. More
recently however, several authors have pointed out that for certain datasets,
hyperbolic target spa... | computer science |
37,901 | ProDiGe: PRioritization Of Disease Genes with multitask machine learning
from positive and unlabeled examples | stat.ML | Elucidating the genetic basis of human diseases is a central goal of genetics
and molecular biology. While traditional linkage analysis and modern
high-throughput techniques often provide long lists of tens or hundreds of
disease gene candidates, the identification of disease genes among the
candidates remains time-con... | computer science |
37,902 | Beta processes, stick-breaking, and power laws | stat.ME | The beta-Bernoulli process provides a Bayesian nonparametric prior for models
involving collections of binary-valued features. A draw from the beta process
yields an infinite collection of probabilities in the unit interval, and a draw
from the Bernoulli process turns these into binary-valued features. Recent work
has ... | computer science |
37,903 | Moment based estimation of stochastic Kronecker graph parameters | stat.ML | Stochastic Kronecker graphs supply a parsimonious model for large sparse real
world graphs. They can specify the distribution of a large random graph using
only three or four parameters. Those parameters have however proved difficult
to choose in specific applications. This article looks at method of moments
estimators... | computer science |
37,904 | A Tutorial on Bayesian Nonparametric Models | stat.ML | A key problem in statistical modeling is model selection, how to choose a
model at an appropriate level of complexity. This problem appears in many
settings, most prominently in choosing the number ofclusters in mixture models
or the number of factors in factor analysis. In this tutorial we describe
Bayesian nonparamet... | computer science |
37,905 | The group fused Lasso for multiple change-point detection | stat.ML | We present the group fused Lasso for detection of multiple change-points
shared by a set of co-occurring one-dimensional signals. Change-points are
detected by approximating the original signals with a constraint on the
multidimensional total variation, leading to piecewise-constant approximations.
Fast algorithms are ... | computer science |
37,906 | Gaussian Process Regression with a Student-t Likelihood | stat.ML | This paper considers the robust and efficient implementation of Gaussian
process regression with a Student-t observation model. The challenge with the
Student-t model is the analytically intractable inference which is why several
approximative methods have been proposed. The expectation propagation (EP) has
been found ... | computer science |
37,907 | Exact covariance thresholding into connected components for large-scale
Graphical Lasso | stat.ML | We consider the sparse inverse covariance regularization problem or graphical
lasso with regularization parameter $\rho$. Suppose the co- variance graph
formed by thresholding the entries of the sample covariance matrix at $\rho$ is
decomposed into connected components. We show that the vertex-partition induced
by the ... | computer science |
37,908 | Semi-supervised logistic discrimination via labeled data and unlabeled
data from different sampling distributions | stat.ML | This article addresses the problem of classification method based on both
labeled and unlabeled data, where we assume that a density function for labeled
data is different from that for unlabeled data. We propose a semi-supervised
logistic regression model for classification problem along with the technique
of covariat... | computer science |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.