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31,702 | Leading strategies in competitive on-line prediction | cs.LG | We start from a simple asymptotic result for the problem of on-line
regression with the quadratic loss function: the class of continuous
limited-memory prediction strategies admits a "leading prediction strategy",
which not only asymptotically performs at least as well as any continuous
limited-memory strategy but also... | computer science |
31,703 | Competing with Markov prediction strategies | cs.LG | Assuming that the loss function is convex in the prediction, we construct a
prediction strategy universal for the class of Markov prediction strategies,
not necessarily continuous. Allowing randomization, we remove the requirement
of convexity. | computer science |
31,704 | A Study on Learnability for Rigid Lambek Grammars | cs.LG | We present basic notions of Gold's "learnability in the limit" paradigm,
first presented in 1967, a formalization of the cognitive process by which a
native speaker gets to grasp the underlying grammar of his/her own native
language by being exposed to well formed sentences generated by that grammar.
Then we present La... | computer science |
31,705 | A Massive Local Rules Search Approach to the Classification Problem | cs.LG | An approach to the classification problem of machine learning, based on
building local classification rules, is developed. The local rules are
considered as projections of the global classification rules to the event we
want to classify. A massive global optimization algorithm is used for
optimization of quality criter... | computer science |
31,706 | Metric entropy in competitive on-line prediction | cs.LG | Competitive on-line prediction (also known as universal prediction of
individual sequences) is a strand of learning theory avoiding making any
stochastic assumptions about the way the observations are generated. The
predictor's goal is to compete with a benchmark class of prediction rules,
which is often a proper Banac... | computer science |
31,707 | PAC Learning Mixtures of Axis-Aligned Gaussians with No Separation
Assumption | cs.LG | We propose and analyze a new vantage point for the learning of mixtures of
Gaussians: namely, the PAC-style model of learning probability distributions
introduced by Kearns et al. Here the task is to construct a hypothesis mixture
of Gaussians that is statistically indistinguishable from the actual mixture
generating t... | computer science |
31,708 | Hedging predictions in machine learning | cs.LG | Recent advances in machine learning make it possible to design efficient
prediction algorithms for data sets with huge numbers of parameters. This paper
describes a new technique for "hedging" the predictions output by many such
algorithms, including support vector machines, kernel ridge regression, kernel
nearest neig... | computer science |
31,709 | A Unified View of TD Algorithms; Introducing Full-Gradient TD and
Equi-Gradient Descent TD | cs.LG | This paper addresses the issue of policy evaluation in Markov Decision
Processes, using linear function approximation. It provides a unified view of
algorithms such as TD(lambda), LSTD(lambda), iLSTD, residual-gradient TD. It is
asserted that they all consist in minimizing a gradient function and differ by
the form of ... | computer science |
31,710 | Bandit Algorithms for Tree Search | cs.LG | Bandit based methods for tree search have recently gained popularity when
applied to huge trees, e.g. in the game of go (Gelly et al., 2006). The UCT
algorithm (Kocsis and Szepesvari, 2006), a tree search method based on Upper
Confidence Bounds (UCB) (Auer et al., 2002), is believed to adapt locally to
the effective sm... | computer science |
31,711 | Intrinsic dimension of a dataset: what properties does one expect? | cs.LG | We propose an axiomatic approach to the concept of an intrinsic dimension of
a dataset, based on a viewpoint of geometry of high-dimensional structures. Our
first axiom postulates that high values of dimension be indicative of the
presence of the curse of dimensionality (in a certain precise mathematical
sense). The se... | computer science |
31,712 | HMM Speaker Identification Using Linear and Non-linear Merging
Techniques | cs.LG | Speaker identification is a powerful, non-invasive and in-expensive biometric
technique. The recognition accuracy, however, deteriorates when noise levels
affect a specific band of frequency. In this paper, we present a sub-band based
speaker identification that intends to improve the live testing performance.
Each fre... | computer science |
31,713 | Scale-sensitive Psi-dimensions: the Capacity Measures for Classifiers
Taking Values in R^Q | cs.LG | Bounds on the risk play a crucial role in statistical learning theory. They
usually involve as capacity measure of the model studied the VC dimension or
one of its extensions. In classification, such "VC dimensions" exist for models
taking values in {0, 1}, {1,..., Q} and R. We introduce the generalizations
appropriate... | computer science |
31,714 | Consistency of the group Lasso and multiple kernel learning | cs.LG | We consider the least-square regression problem with regularization by a
block 1-norm, i.e., a sum of Euclidean norms over spaces of dimensions larger
than one. This problem, referred to as the group Lasso, extends the usual
regularization by the 1-norm where all spaces have dimension one, where it is
commonly referred... | computer science |
31,715 | Cost-minimising strategies for data labelling : optimal stopping and
active learning | cs.LG | Supervised learning deals with the inference of a distribution over an output
or label space $\CY$ conditioned on points in an observation space $\CX$, given
a training dataset $D$ of pairs in $\CX \times \CY$. However, in a lot of
applications of interest, acquisition of large amounts of observations is easy,
while th... | computer science |
31,716 | Defensive forecasting for optimal prediction with expert advice | cs.LG | The method of defensive forecasting is applied to the problem of prediction
with expert advice for binary outcomes. It turns out that defensive forecasting
is not only competitive with the Aggregating Algorithm but also handles the
case of "second-guessing" experts, whose advice depends on the learner's
prediction; thi... | computer science |
31,717 | Continuous and randomized defensive forecasting: unified view | cs.LG | Defensive forecasting is a method of transforming laws of probability (stated
in game-theoretic terms as strategies for Sceptic) into forecasting algorithms.
There are two known varieties of defensive forecasting: "continuous", in which
Sceptic's moves are assumed to depend on the forecasts in a (semi)continuous
manner... | computer science |
31,718 | On the Relationship between the Posterior and Optimal Similarity | cs.LG | For a classification problem described by the joint density $P(\omega,x)$,
models of $P(\omega\eq\omega'|x,x')$ (the ``Bayesian similarity measure'') have
been shown to be an optimal similarity measure for nearest neighbor
classification. This paper analyzes demonstrates several additional properties
of that conditiona... | computer science |
31,719 | Equations of States in Singular Statistical Estimation | cs.LG | Learning machines which have hierarchical structures or hidden variables are
singular statistical models because they are nonidentifiable and their Fisher
information matrices are singular. In singular statistical models, neither the
Bayes a posteriori distribution converges to the normal distribution nor the
maximum l... | computer science |
31,720 | Density estimation in linear time | cs.LG | We consider the problem of choosing a density estimate from a set of
distributions F, minimizing the L1-distance to an unknown distribution
(Devroye, Lugosi 2001). Devroye and Lugosi analyze two algorithms for the
problem: Scheffe tournament winner and minimum distance estimate. The Scheffe
tournament estimate requires... | computer science |
31,721 | Graph kernels between point clouds | cs.LG | Point clouds are sets of points in two or three dimensions. Most kernel
methods for learning on sets of points have not yet dealt with the specific
geometrical invariances and practical constraints associated with point clouds
in computer vision and graphics. In this paper, we present extensions of graph
kernels for po... | computer science |
31,722 | Online variants of the cross-entropy method | cs.LG | The cross-entropy method is a simple but efficient method for global
optimization. In this paper we provide two online variants of the basic CEM,
together with a proof of convergence. | computer science |
31,723 | The optimal assignment kernel is not positive definite | cs.LG | We prove that the optimal assignment kernel, proposed recently as an attempt
to embed labeled graphs and more generally tuples of basic data to a Hilbert
space, is in fact not always positive definite. | computer science |
31,724 | New Estimation Procedures for PLS Path Modelling | cs.LG | Given R groups of numerical variables X1, ... XR, we assume that each group
is the result of one underlying latent variable, and that all latent variables
are bound together through a linear equation system. Moreover, we assume that
some explanatory latent variables may interact pairwise in one or more
equations. We ba... | computer science |
31,725 | A New Approach to Collaborative Filtering: Operator Estimation with
Spectral Regularization | cs.LG | We present a general approach for collaborative filtering (CF) using spectral
regularization to learn linear operators from "users" to the "objects" they
rate. Recent low-rank type matrix completion approaches to CF are shown to be
special cases. However, unlike existing regularization based CF methods, our
approach ca... | computer science |
31,726 | Multiple Random Oracles Are Better Than One | cs.LG | We study the problem of learning k-juntas given access to examples drawn from
a number of different product distributions. Thus we wish to learn a function f
: {-1,1}^n -> {-1,1} that depends on k (unknown) coordinates. While the best
known algorithms for the general problem of learning a k-junta require running
time o... | computer science |
31,727 | Introduction to Relational Networks for Classification | cs.LG | The use of computational intelligence techniques for classification has been
used in numerous applications. This paper compares the use of a Multi Layer
Perceptron Neural Network and a new Relational Network on classifying the HIV
status of women at ante-natal clinics. The paper discusses the architecture of
the relati... | computer science |
31,728 | The Effect of Structural Diversity of an Ensemble of Classifiers on
Classification Accuracy | cs.LG | This paper aims to showcase the measure of structural diversity of an
ensemble of 9 classifiers and then map a relationship between this structural
diversity and accuracy. The structural diversity was induced by having
different architectures or structures of the classifiers The Genetical
Algorithms (GA) were used to d... | computer science |
31,729 | A Quadratic Loss Multi-Class SVM | cs.LG | Using a support vector machine requires to set two types of hyperparameters:
the soft margin parameter C and the parameters of the kernel. To perform this
model selection task, the method of choice is cross-validation. Its
leave-one-out variant is known to produce an estimator of the generalization
error which is almos... | computer science |
31,730 | On Recovery of Sparse Signals via $\ell_1$ Minimization | cs.LG | This article considers constrained $\ell_1$ minimization methods for the
recovery of high dimensional sparse signals in three settings: noiseless,
bounded error and Gaussian noise. A unified and elementary treatment is given
in these noise settings for two $\ell_1$ minimization methods: the Dantzig
selector and $\ell_1... | computer science |
31,731 | The Margitron: A Generalised Perceptron with Margin | cs.LG | We identify the classical Perceptron algorithm with margin as a member of a
broader family of large margin classifiers which we collectively call the
Margitron. The Margitron, (despite its) sharing the same update rule with the
Perceptron, is shown in an incremental setting to converge in a finite number
of updates to ... | computer science |
31,732 | Sample Selection Bias Correction Theory | cs.LG | This paper presents a theoretical analysis of sample selection bias
correction. The sample bias correction technique commonly used in machine
learning consists of reweighting the cost of an error on each training point of
a biased sample to more closely reflect the unbiased distribution. This relies
on weights derived ... | computer science |
31,733 | From Data Topology to a Modular Classifier | cs.LG | This article describes an approach to designing a distributed and modular
neural classifier. This approach introduces a new hierarchical clustering that
enables one to determine reliable regions in the representation space by
exploiting supervised information. A multilayer perceptron is then associated
with each of the... | computer science |
31,734 | Utilisation des grammaires probabilistes dans les tâches de
segmentation et d'annotation prosodique | cs.LG | Nous pr\'esentons dans cette contribution une approche \`a la fois symbolique
et probabiliste permettant d'extraire l'information sur la segmentation du
signal de parole \`a partir d'information prosodique. Nous utilisons pour ce
faire des grammaires probabilistes poss\'edant une structure hi\'erarchique
minimale. La p... | computer science |
31,735 | Statistical Learning of Arbitrary Computable Classifiers | cs.LG | Statistical learning theory chiefly studies restricted hypothesis classes,
particularly those with finite Vapnik-Chervonenkis (VC) dimension. The
fundamental quantity of interest is the sample complexity: the number of
samples required to learn to a specified level of accuracy. Here we consider
learning over the set of... | computer science |
31,736 | Agnostically Learning Juntas from Random Walks | cs.LG | We prove that the class of functions g:{-1,+1}^n -> {-1,+1} that only depend
on an unknown subset of k<<n variables (so-called k-juntas) is agnostically
learnable from a random walk in time polynomial in n, 2^{k^2}, epsilon^{-k},
and log(1/delta). In other words, there is an algorithm with the claimed
running time that... | computer science |
31,737 | Computationally Efficient Estimators for Dimension Reductions Using
Stable Random Projections | cs.LG | The method of stable random projections is a tool for efficiently computing
the $l_\alpha$ distances using low memory, where $0<\alpha \leq 2$ is a tuning
parameter. The method boils down to a statistical estimation task and various
estimators have been proposed, based on the geometric mean, the harmonic mean,
and the ... | computer science |
31,738 | On Approximating the Lp Distances for p>2 | cs.LG | Applications in machine learning and data mining require computing pairwise
Lp distances in a data matrix A. For massive high-dimensional data, computing
all pairwise distances of A can be infeasible. In fact, even storing A or all
pairwise distances of A in the memory may be also infeasible. This paper
proposes a simp... | computer science |
31,739 | Graph Kernels | cs.LG | We present a unified framework to study graph kernels, special cases of which
include the random walk graph kernel \citep{GaeFlaWro03,BorOngSchVisetal05},
marginalized graph kernel \citep{KasTsuIno03,KasTsuIno04,MahUedAkuPeretal04},
and geometric kernel on graphs \citep{Gaertner02}. Through extensions of linear
algebra... | computer science |
31,740 | On Probability Distributions for Trees: Representations, Inference and
Learning | cs.LG | We study probability distributions over free algebras of trees. Probability
distributions can be seen as particular (formal power) tree series [Berstel et
al 82, Esik et al 03], i.e. mappings from trees to a semiring K . A widely
studied class of tree series is the class of rational (or recognizable) tree
series which ... | computer science |
31,741 | Positive factor networks: A graphical framework for modeling
non-negative sequential data | cs.LG | We present a novel graphical framework for modeling non-negative sequential
data with hierarchical structure. Our model corresponds to a network of coupled
non-negative matrix factorization (NMF) modules, which we refer to as a
positive factor network (PFN). The data model is linear, subject to
non-negativity constrain... | computer science |
31,742 | When is there a representer theorem? Vector versus matrix regularizers | cs.LG | We consider a general class of regularization methods which learn a vector of
parameters on the basis of linear measurements. It is well known that if the
regularizer is a nondecreasing function of the inner product then the learned
vector is a linear combination of the input data. This result, known as the
{\em repres... | computer science |
31,743 | Clustered Multi-Task Learning: A Convex Formulation | cs.LG | In multi-task learning several related tasks are considered simultaneously,
with the hope that by an appropriate sharing of information across tasks, each
task may benefit from the others. In the context of learning linear functions
for supervised classification or regression, this can be achieved by including
a priori... | computer science |
31,744 | Surrogate Learning - An Approach for Semi-Supervised Classification | cs.LG | We consider the task of learning a classifier from the feature space
$\mathcal{X}$ to the set of classes $\mathcal{Y} = \{0, 1\}$, when the features
can be partitioned into class-conditionally independent feature sets
$\mathcal{X}_1$ and $\mathcal{X}_2$. We show the surprising fact that the
class-conditional independen... | computer science |
31,745 | Entropy, Perception, and Relativity | cs.LG | In this paper, I expand Shannon's definition of entropy into a new form of
entropy that allows integration of information from different random events.
Shannon's notion of entropy is a special case of my more general definition of
entropy. I define probability using a so-called performance function, which is
de facto a... | computer science |
31,746 | Stability Bound for Stationary Phi-mixing and Beta-mixing Processes | cs.LG | Most generalization bounds in learning theory are based on some measure of
the complexity of the hypothesis class used, independently of any algorithm. In
contrast, the notion of algorithmic stability can be used to derive tight
generalization bounds that are tailored to specific learning algorithms by
exploiting their... | computer science |
31,747 | Land Cover Mapping Using Ensemble Feature Selection Methods | cs.LG | Ensemble classification is an emerging approach to land cover mapping whereby
the final classification output is a result of a consensus of classifiers.
Intuitively, an ensemble system should consist of base classifiers which are
diverse i.e. classifiers whose decision boundaries err differently. In this
paper ensemble... | computer science |
31,748 | Distributed Preemption Decisions: Probabilistic Graphical Model,
Algorithm and Near-Optimality | cs.LG | Cooperative decision making is a vision of future network management and
control. Distributed connection preemption is an important example where nodes
can make intelligent decisions on allocating resources and controlling traffic
flows for multi-class service networks. A challenge is that nodal decisions are
spatially... | computer science |
31,749 | A Limit Theorem in Singular Regression Problem | cs.LG | In statistical problems, a set of parameterized probability distributions is
used to estimate the true probability distribution. If Fisher information
matrix at the true distribution is singular, then it has been left unknown what
we can estimate about the true distribution from random samples. In this paper,
we study ... | computer science |
31,750 | Cross-situational and supervised learning in the emergence of
communication | cs.LG | Scenarios for the emergence or bootstrap of a lexicon involve the repeated
interaction between at least two agents who must reach a consensus on how to
name N objects using H words. Here we consider minimal models of two types of
learning algorithms: cross-situational learning, in which the individuals
determine the me... | computer science |
31,751 | Extraction de concepts sous contraintes dans des données d'expression
de gènes | cs.LG | In this paper, we propose a technique to extract constrained formal concepts. | computer science |
31,752 | Database Transposition for Constrained (Closed) Pattern Mining | cs.LG | Recently, different works proposed a new way to mine patterns in databases
with pathological size. For example, experiments in genome biology usually
provide databases with thousands of attributes (genes) but only tens of objects
(experiments). In this case, mining the "transposed" database runs through a
smaller searc... | computer science |
31,753 | Multi-Label Prediction via Compressed Sensing | cs.LG | We consider multi-label prediction problems with large output spaces under
the assumption of output sparsity -- that the target (label) vectors have small
support. We develop a general theory for a variant of the popular error
correcting output code scheme, using ideas from compressed sensing for
exploiting this sparsi... | computer science |
31,754 | Learning rules from multisource data for cardiac monitoring | cs.LG | This paper formalises the concept of learning symbolic rules from multisource
data in a cardiac monitoring context. Our sources, electrocardiograms and
arterial blood pressure measures, describe cardiac behaviours from different
viewpoints. To learn interpretable rules, we use an Inductive Logic Programming
(ILP) metho... | computer science |
31,755 | Uniqueness of Low-Rank Matrix Completion by Rigidity Theory | cs.LG | The problem of completing a low-rank matrix from a subset of its entries is
often encountered in the analysis of incomplete data sets exhibiting an
underlying factor model with applications in collaborative filtering, computer
vision and control. Most recent work had been focused on constructing efficient
algorithms fo... | computer science |
31,756 | Prediction with expert evaluators' advice | cs.LG | We introduce a new protocol for prediction with expert advice in which each
expert evaluates the learner's and his own performance using a loss function
that may change over time and may be different from the loss functions used by
the other experts. The learner's goal is to perform better or not much worse
than each e... | computer science |
31,757 | Multiplicative updates For Non-Negative Kernel SVM | cs.LG | We present multiplicative updates for solving hard and soft margin support
vector machines (SVM) with non-negative kernels. They follow as a natural
extension of the updates for non-negative matrix factorization. No additional
param- eter setting, such as choosing learning, rate is required. Ex- periments
demonstrate r... | computer science |
31,758 | Stability Analysis and Learning Bounds for Transductive Regression
Algorithms | cs.LG | This paper uses the notion of algorithmic stability to derive novel
generalization bounds for several families of transductive regression
algorithms, both by using convexity and closed-form solutions. Our analysis
helps compare the stability of these algorithms. It also shows that a number of
widely used transductive r... | computer science |
31,759 | Inferring Dynamic Bayesian Networks using Frequent Episode Mining | cs.LG | Motivation: Several different threads of research have been proposed for
modeling and mining temporal data. On the one hand, approaches such as dynamic
Bayesian networks (DBNs) provide a formal probabilistic basis to model
relationships between time-indexed random variables but these models are
intractable to learn in ... | computer science |
31,760 | Introduction to Machine Learning: Class Notes 67577 | cs.LG | Introduction to Machine learning covering Statistical Inference (Bayes, EM,
ML/MaxEnt duality), algebraic and spectral methods (PCA, LDA, CCA, Clustering),
and PAC learning (the Formal model, VC dimension, Double Sampling theorem). | computer science |
31,761 | Limits of Learning about a Categorical Latent Variable under Prior
Near-Ignorance | cs.LG | In this paper, we consider the coherent theory of (epistemic) uncertainty of
Walley, in which beliefs are represented through sets of probability
distributions, and we focus on the problem of modeling prior ignorance about a
categorical random variable. In this setting, it is a known result that a state
of prior ignora... | computer science |
31,762 | Temporal data mining for root-cause analysis of machine faults in
automotive assembly lines | cs.LG | Engine assembly is a complex and heavily automated distributed-control
process, with large amounts of faults data logged everyday. We describe an
application of temporal data mining for analyzing fault logs in an engine
assembly plant. Frequent episode discovery framework is a model-free method
that can be used to dedu... | computer science |
31,763 | Combining Supervised and Unsupervised Learning for GIS Classification | cs.LG | This paper presents a new hybrid learning algorithm for unsupervised
classification tasks. We combined Fuzzy c-means learning algorithm and a
supervised version of Minimerror to develop a hybrid incremental strategy
allowing unsupervised classifications. We applied this new approach to a
real-world database in order to... | computer science |
31,764 | Average-Case Active Learning with Costs | cs.LG | We analyze the expected cost of a greedy active learning algorithm. Our
analysis extends previous work to a more general setting in which different
queries have different costs. Moreover, queries may have more than two possible
responses and the distribution over hypotheses may be non uniform. Specific
applications inc... | computer science |
31,765 | Transfer Learning Using Feature Selection | cs.LG | We present three related ways of using Transfer Learning to improve feature
selection. The three methods address different problems, and hence share
different kinds of information between tasks or feature classes, but all three
are based on the information theoretic Minimum Description Length (MDL)
principle and share ... | computer science |
31,766 | Clustering for Improved Learning in Maze Traversal Problem | cs.LG | The maze traversal problem (finding the shortest distance to the goal from
any position in a maze) has been an interesting challenge in computational
intelligence. Recent work has shown that the cellular simultaneous recurrent
neural network (CSRN) can solve this problem for simple mazes. This thesis
focuses on exploit... | computer science |
31,767 | A Mirroring Theorem and its Application to a New Method of Unsupervised
Hierarchical Pattern Classification | cs.LG | In this paper, we prove a crucial theorem called Mirroring Theorem which
affirms that given a collection of samples with enough information in it such
that it can be classified into classes and subclasses then (i) There exists a
mapping which classifies and subclassifies these samples (ii) There exists a
hierarchical c... | computer science |
31,768 | Sequential anomaly detection in the presence of noise and limited
feedback | cs.LG | This paper describes a methodology for detecting anomalies from sequentially
observed and potentially noisy data. The proposed approach consists of two main
elements: (1) {\em filtering}, or assigning a belief or likelihood to each
successive measurement based upon our ability to predict it from previous noisy
observat... | computer science |
31,769 | Keystroke Dynamics Authentication For Collaborative Systems | cs.LG | We present in this paper a study on the ability and the benefits of using a
keystroke dynamics authentication method for collaborative systems.
Authentication is a challenging issue in order to guarantee the security of use
of collaborative systems during the access control step. Many solutions exist
in the state of th... | computer science |
31,770 | Statistical exponential families: A digest with flash cards | cs.LG | This document describes concisely the ubiquitous class of exponential family
distributions met in statistics. The first part recalls definitions and
summarizes main properties and duality with Bregman divergences (all proofs are
skipped). The second part lists decompositions and related formula of common
exponential fa... | computer science |
31,771 | Learning Mixtures of Gaussians using the k-means Algorithm | cs.LG | One of the most popular algorithms for clustering in Euclidean space is the
$k$-means algorithm; $k$-means is difficult to analyze mathematically, and few
theoretical guarantees are known about it, particularly when the data is {\em
well-clustered}. In this paper, we attempt to fill this gap in the literature
by analyz... | computer science |
31,772 | Delay-Optimal Power and Subcarrier Allocation for OFDMA Systems via
Stochastic Approximation | cs.LG | In this paper, we consider delay-optimal power and subcarrier allocation
design for OFDMA systems with $N_F$ subcarriers, $K$ mobiles and one base
station. There are $K$ queues at the base station for the downlink traffic to
the $K$ mobiles with heterogeneous packet arrivals and delay requirements. We
shall model the p... | computer science |
31,773 | Association Rule Pruning based on Interestingness Measures with
Clustering | cs.LG | Association rule mining plays vital part in knowledge mining. The difficult
task is discovering knowledge or useful rules from the large number of rules
generated for reduced support. For pruning or grouping rules, several
techniques are used such as rule structure cover methods, informative cover
methods, rule cluster... | computer science |
31,774 | Early Detection of Breast Cancer using SVM Classifier Technique | cs.LG | This paper presents a tumor detection algorithm from mammogram. The proposed
system focuses on the solution of two problems. One is how to detect tumors as
suspicious regions with a very weak contrast to their background and another is
how to extract features which categorize tumors. The tumor detection method
follows ... | computer science |
31,775 | Performance Analysis of AIM-K-means & K-means in Quality Cluster
Generation | cs.LG | Among all the partition based clustering algorithms K-means is the most
popular and well known method. It generally shows impressive results even in
considerably large data sets. The computational complexity of K-means does not
suffer from the size of the data set. The main disadvantage faced in performing
this cluster... | computer science |
31,776 | Gaussian Process Optimization in the Bandit Setting: No Regret and
Experimental Design | cs.LG | Many applications require optimizing an unknown, noisy function that is
expensive to evaluate. We formalize this task as a multi-armed bandit problem,
where the payoff function is either sampled from a Gaussian process (GP) or has
low RKHS norm. We resolve the important open problem of deriving regret bounds
for this s... | computer science |
31,777 | Aggregating Algorithm competing with Banach lattices | cs.LG | The paper deals with on-line regression settings with signals belonging to a
Banach lattice. Our algorithms work in a semi-online setting where all the
inputs are known in advance and outcomes are unknown and given step by step. We
apply the Aggregating Algorithm to construct a prediction method whose
cumulative loss o... | computer science |
31,778 | A CHAID Based Performance Prediction Model in Educational Data Mining | cs.LG | The performance in higher secondary school education in India is a turning
point in the academic lives of all students. As this academic performance is
influenced by many factors, it is essential to develop predictive data mining
model for students' performance so as to identify the slow learners and study
the influenc... | computer science |
31,779 | Dimensionality Reduction: An Empirical Study on the Usability of IFE-CF
(Independent Feature Elimination- by C-Correlation and F-Correlation)
Measures | cs.LG | The recent increase in dimensionality of data has thrown a great challenge to
the existing dimensionality reduction methods in terms of their effectiveness.
Dimensionality reduction has emerged as one of the significant preprocessing
steps in machine learning applications and has been effective in removing
inappropriat... | computer science |
31,780 | Online Distributed Sensor Selection | cs.LG | A key problem in sensor networks is to decide which sensors to query when, in
order to obtain the most useful information (e.g., for performing accurate
prediction), subject to constraints (e.g., on power and bandwidth). In many
applications the utility function is not known a priori, must be learned from
data, and can... | computer science |
31,781 | On the Stability of Empirical Risk Minimization in the Presence of
Multiple Risk Minimizers | cs.LG | Recently Kutin and Niyogi investigated several notions of algorithmic
stability--a property of a learning map conceptually similar to
continuity--showing that training-stability is sufficient for consistency of
Empirical Risk Minimization while distribution-free CV-stability is necessary
and sufficient for having finit... | computer science |
31,782 | Collaborative Filtering in a Non-Uniform World: Learning with the
Weighted Trace Norm | cs.LG | We show that matrix completion with trace-norm regularization can be
significantly hurt when entries of the matrix are sampled non-uniformly. We
introduce a weighted version of the trace-norm regularizer that works well also
with non-uniform sampling. Our experimental results demonstrate that the
weighted trace-norm re... | computer science |
31,783 | Interactive Submodular Set Cover | cs.LG | We introduce a natural generalization of submodular set cover and exact
active learning with a finite hypothesis class (query learning). We call this
new problem interactive submodular set cover. Applications include advertising
in social networks with hidden information. We give an approximation guarantee
for a novel ... | computer science |
31,784 | Word level Script Identification from Bangla and Devanagri Handwritten
Texts mixed with Roman Script | cs.LG | India is a multi-lingual country where Roman script is often used alongside
different Indic scripts in a text document. To develop a script specific
handwritten Optical Character Recognition (OCR) system, it is therefore
necessary to identify the scripts of handwritten text correctly. In this paper,
we present a system... | computer science |
31,785 | Contextual Bandit Algorithms with Supervised Learning Guarantees | cs.LG | We address the problem of learning in an online, bandit setting where the
learner must repeatedly select among $K$ actions, but only receives partial
feedback based on its choices. We establish two new facts: First, using a new
algorithm called Exp4.P, we show that it is possible to compete with the best
in a set of $N... | computer science |
31,786 | Adaptive Bound Optimization for Online Convex Optimization | cs.LG | We introduce a new online convex optimization algorithm that adaptively
chooses its regularization function based on the loss functions observed so
far. This is in contrast to previous algorithms that use a fixed regularization
function such as L2-squared, and modify it only via a single time-dependent
parameter. Our a... | computer science |
31,787 | State-Space Dynamics Distance for Clustering Sequential Data | cs.LG | This paper proposes a novel similarity measure for clustering sequential
data. We first construct a common state-space by training a single
probabilistic model with all the sequences in order to get a unified
representation for the dataset. Then, distances are obtained attending to the
transition matrices induced by ea... | computer science |
31,788 | Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable
Information Criterion in Singular Learning Theory | cs.LG | In regular statistical models, the leave-one-out cross-validation is
asymptotically equivalent to the Akaike information criterion. However, since
many learning machines are singular statistical models, the asymptotic behavior
of the cross-validation remains unknown. In previous studies, we established
the singular lea... | computer science |
31,789 | Generation and Interpretation of Temporal Decision Rules | cs.LG | We present a solution to the problem of understanding a system that produces
a sequence of temporally ordered observations. Our solution is based on
generating and interpreting a set of temporal decision rules. A temporal
decision rule is a decision rule that can be used to predict or retrodict the
value of a decision ... | computer science |
31,790 | Bregman Distance to L1 Regularized Logistic Regression | cs.LG | In this work we investigate the relationship between Bregman distances and
regularized Logistic Regression model. We present a detailed study of Bregman
Distance minimization, a family of generalized entropy measures associated with
convex functions. We convert the L1-regularized logistic regression into this
more gene... | computer science |
31,791 | Efficient Learning with Partially Observed Attributes | cs.LG | We describe and analyze efficient algorithms for learning a linear predictor
from examples when the learner can only view a few attributes of each training
example. This is the case, for instance, in medical research, where each
patient participating in the experiment is only willing to go through a small
number of tes... | computer science |
31,792 | Learning from Multiple Outlooks | cs.LG | We propose a novel problem formulation of learning a single task when the
data are provided in different feature spaces. Each such space is called an
outlook, and is assumed to contain both labeled and unlabeled data. The
objective is to take advantage of the data from all the outlooks to better
classify each of the ou... | computer science |
31,793 | A Geometric View of Conjugate Priors | cs.LG | In Bayesian machine learning, conjugate priors are popular, mostly due to
mathematical convenience. In this paper, we show that there are deeper reasons
for choosing a conjugate prior. Specifically, we formulate the conjugate prior
in the form of Bregman divergence and show that it is the inherent geometry of
conjugate... | computer science |
31,794 | Distributive Stochastic Learning for Delay-Optimal OFDMA Power and
Subband Allocation | cs.LG | In this paper, we consider the distributive queue-aware power and subband
allocation design for a delay-optimal OFDMA uplink system with one base
station, $K$ users and $N_F$ independent subbands. Each mobile has an uplink
queue with heterogeneous packet arrivals and delay requirements. We model the
problem as an infin... | computer science |
31,795 | Statistical Learning in Automated Troubleshooting: Application to LTE
Interference Mitigation | cs.LG | This paper presents a method for automated healing as part of off-line
automated troubleshooting. The method combines statistical learning with
constraint optimization. The automated healing aims at locally optimizing radio
resource management (RRM) or system parameters of cells with poor performance
in an iterative ma... | computer science |
31,796 | The Complex Gaussian Kernel LMS algorithm | cs.LG | Although the real reproducing kernels are used in an increasing number of
machine learning problems, complex kernels have not, yet, been used, in spite
of their potential interest in applications such as communications. In this
work, we focus our attention on the complex gaussian kernel and its possible
application in ... | computer science |
31,797 | Extension of Wirtinger Calculus in RKH Spaces and the Complex Kernel LMS | cs.LG | Over the last decade, kernel methods for nonlinear processing have
successfully been used in the machine learning community. However, so far, the
emphasis has been on batch techniques. It is only recently, that online
adaptive techniques have been considered in the context of signal processing
tasks. To the best of our... | computer science |
31,798 | Improving Semi-Supervised Support Vector Machines Through Unlabeled
Instances Selection | cs.LG | Semi-supervised support vector machines (S3VMs) are a kind of popular
approaches which try to improve learning performance by exploiting unlabeled
data. Though S3VMs have been found helpful in many situations, they may
degenerate performance and the resultant generalization ability may be even
worse than using the labe... | computer science |
31,799 | Prediction with Expert Advice under Discounted Loss | cs.LG | We study prediction with expert advice in the setting where the losses are
accumulated with some discounting---the impact of old losses may gradually
vanish. We generalize the Aggregating Algorithm and the Aggregating Algorithm
for Regression to this case, propose a suitable new variant of exponential
weights algorithm... | computer science |
31,800 | Detecting Blackholes and Volcanoes in Directed Networks | cs.LG | In this paper, we formulate a novel problem for finding blackhole and volcano
patterns in a large directed graph. Specifically, a blackhole pattern is a
group which is made of a set of nodes in a way such that there are only inlinks
to this group from the rest nodes in the graph. In contrast, a volcano pattern
is a gro... | computer science |
31,801 | Robustness and Generalization | cs.LG | We derive generalization bounds for learning algorithms based on their
robustness: the property that if a testing sample is "similar" to a training
sample, then the testing error is close to the training error. This provides a
novel approach, different from the complexity or stability arguments, to study
generalization... | computer science |
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