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In this contribution, we propose a generic online (also sometimes called
adaptive or recursive) version of the Expectation-Maximisation (EM) algorithm
applicable to latent variable models of independent observations. Compared to
the algorithm of Titterington (1984), this approach is more directly connected
to the usu... | 2,017 |
Online EM Algorithm for Latent Data Models | 2,017 |
We define a new model of quantum learning that we call Predictive Quantum
(PQ). This is a quantum analogue of PAC, where during the testing phase the
student is only required to answer a polynomial number of testing queries.
We demonstrate a relational concept class that is efficiently learnable in
PQ, while in any... | 2,012 |
Quantum Predictive Learning and Communication Complexity with Single
Input | 2,012 |
Cilibrasi and Vitanyi have demonstrated that it is possible to extract the
meaning of words from the world-wide web. To achieve this, they rely on the
number of webpages that are found through a Google search containing a given
word and they associate the page count to the probability that the word appears
on a webpa... | 2,015 |
Google distance between words | 2,015 |
This paper has been retracted.
| 2,013 |
Differential Contrastive Divergence | 2,013 |
Given a matrix M of low-rank, we consider the problem of reconstructing it
from noisy observations of a small, random subset of its entries. The problem
arises in a variety of applications, from collaborative filtering (the `Netflix
problem') to structure-from-motion and positioning. We study a low complexity
algorit... | 2,012 |
Matrix Completion from Noisy Entries | 2,012 |
Background: Hidden Markov models are widely employed by numerous
bioinformatics programs used today. Applications range widely from comparative
gene prediction to time-series analyses of micro-array data. The parameters of
the underlying models need to be adjusted for specific data sets, for example
the genome of a p... | 2,012 |
Efficient algorithms for training the parameters of hidden Markov models
using stochastic expectation maximization EM training and Viterbi training | 2,012 |
There are many resources useful for processing images, most of them freely
available and quite friendly to use. In spite of this abundance of tools, a
study of the processing methods is still worthy of efforts. Here, we want to
discuss the possibilities arising from the use of fractional differential
calculus. This c... | 2,015 |
Fractional differentiation based image processing | 2,015 |
The versatility of exponential families, along with their attendant convexity
properties, make them a popular and effective statistical model. A central
issue is learning these models in high-dimensions, such as when there is some
sparsity pattern of the optimal parameter. This work characterizes a certain
strong con... | 2,015 |
Learning Exponential Families in High-Dimensions: Strong Convexity and
Sparsity | 2,015 |
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
observ... | 2,012 |
Sequential anomaly detection in the presence of noise and limited
feedback | 2,012 |
Languages evolve over time in a process in which reproduction, mutation and
extinction are all possible, similar to what happens to living organisms. Using
this similarity it is possible, in principle, to build family trees which show
the degree of relatedness between languages.
The method used by modern glottochro... | 2,012 |
Automated languages phylogeny from Levenshtein distance | 2,012 |
Analogical reasoning depends fundamentally on the ability to learn and
generalize about relations between objects. We develop an approach to
relational learning which, given a set of pairs of objects
$\mathbf{S}=\{A^{(1)}:B^{(1)},A^{(2)}:B^{(2)},\ldots,A^{(N)}:B ^{(N)}\}$,
measures how well other pairs A:B fit in wit... | 2,013 |
Ranking relations using analogies in biological and information networks | 2,013 |
We consider a group of Bayesian agents who try to estimate a state of the
world $\theta$ through interaction on a social network. Each agent $v$
initially receives a private measurement of $\theta$: a number $S_v$ picked
from a Gaussian distribution with mean $\theta$ and standard deviation one.
Then, in each discret... | 2,016 |
Efficient Bayesian Learning in Social Networks with Gaussian Estimators | 2,016 |
Automatic License Plate Recognition (ALPR) is a challenging area of research
due to its importance to variety of commercial applications. The overall
problem may be subdivided into two key modules, firstly, localization of
license plates from vehicle images, and secondly, optical character recognition
of extracted li... | 2,015 |
An Offline Technique for Localization of License Plates for Indian
Commercial Vehicles | 2,015 |
Integrated Traffic Management Systems (ITMS) are now implemented in different
cities in India to primarily address the concerns of road-safety and security.
An automated Red Light Violation Detection System (RLVDS) is an integral part
of the ITMS. In our present work we have designed and developed a complete
system f... | 2,015 |
Development of an automated Red Light Violation Detection System (RLVDS)
for Indian vehicles | 2,015 |
Automatic License Plate Recognition system is a challenging area of research
now-a-days and binarization is an integral and most important part of it. In
case of a real life scenario, most of existing methods fail to properly
binarize the image of a vehicle in a congested road, captured through a CCD
camera. In the c... | 2,015 |
A novel scheme for binarization of vehicle images using hierarchical
histogram equalization technique | 2,015 |
Biological data objects often have both of the following features: (i) they
are functions rather than single numbers or vectors, and (ii) they are
correlated due to phylogenetic relationships. In this paper we give a flexible
statistical model for such data, by combining assumptions from phylogenetics
with Gaussian p... | 2,012 |
Evolutionary Inference for Function-valued Traits: Gaussian Process
Regression on Phylogenies | 2,012 |
We introduce a theory of sequential causal inference in which learners in a
chain estimate a structural model from their upstream teacher and then pass
samples from the model to their downstream student. It extends the population
dynamics of genetic drift, recasting Kimura's selectively neutral theory as a
special ca... | 2,012 |
Structural Drift: The Population Dynamics of Sequential Learning | 2,012 |
We study the problem of estimating high-dimensional regression models
regularized by a structured sparsity-inducing penalty that encodes prior
structural information on either the input or output variables. We consider two
widely adopted types of penalties of this kind as motivating examples: (1) the
general overlapp... | 2,012 |
Smoothing proximal gradient method for general structured sparse
regression | 2,012 |
We consider the problem of sequential prediction and provide tools to study
the minimax value of the associated game. Classical statistical learning theory
provides several useful complexity measures to study learning with i.i.d. data.
Our proposed sequential complexities can be seen as extensions of these
measures t... | 2,014 |
Online Learning via Sequential Complexities | 2,014 |
This companion paper complements the main DEFT'10 article describing the MARF
approach (arXiv:0905.1235) to the DEFT'10 NLP challenge (described at
http://www.groupes.polymtl.ca/taln2010/deft.php in French). This paper is aimed
to present the complete result sets of all the conducted experiments and their
settings in... | 2,014 |
Complete Complementary Results Report of the MARF's NLP Approach to the
DEFT 2010 Competition | 2,014 |
One of the most visually demonstrable and straightforward uses of filtering
is in the field of Computer Vision. In this document we will try to outline the
issues encountered while designing and implementing a particle and kalman
filter based tracking system.
| 2,017 |
3D Visual Tracking with Particle and Kalman Filters | 2,017 |
The two parameter Poisson-Dirichlet Process (PDP), a generalisation of the
Dirichlet Process, is increasingly being used for probabilistic modelling in
discrete areas such as language technology, bioinformatics, and image analysis.
There is a rich literature about the PDP and its derivative distributions such
as the ... | 2,012 |
A Bayesian View of the Poisson-Dirichlet Process | 2,012 |
Motivated by the unceasing interest in hidden Markov models (HMMs), this
paper re-examines hidden path inference in these models, using primarily a
risk-based framework. While the most common maximum a posteriori (MAP), or
Viterbi, path estimator and the minimum error, or Posterior Decoder (PD), have
long been around... | 2,013 |
A generalized risk approach to path inference based on hidden Markov
models | 2,013 |
In this paper we present a new algorithm for learning oblique decision trees.
Most of the current decision tree algorithms rely on impurity measures to
assess the goodness of hyperplanes at each node while learning a decision tree
in a top-down fashion. These impurity measures do not properly capture the
geometric st... | 2,012 |
Geometric Decision Tree | 2,012 |
Margin theory provides one of the most popular explanations to the success of
\texttt{AdaBoost}, where the central point lies in the recognition that
\textit{margin} is the key for characterizing the performance of
\texttt{AdaBoost}. This theory has been very influential, e.g., it has been
used to argue that \texttt{... | 2,013 |
On the Doubt about Margin Explanation of Boosting | 2,013 |
We establish an excess risk bound of O(H R_n^2 + R_n \sqrt{H L*}) for
empirical risk minimization with an H-smooth loss function and a hypothesis
class with Rademacher complexity R_n, where L* is the best risk achievable by
the hypothesis class. For typical hypothesis classes where R_n = \sqrt{R/n},
this translates t... | 2,012 |
Optimistic Rates for Learning with a Smooth Loss | 2,012 |
We consider the problem of energy-efficient point-to-point transmission of
delay-sensitive data (e.g. multimedia data) over a fading channel. Existing
research on this topic utilizes either physical-layer centric solutions, namely
power-control and adaptive modulation and coding (AMC), or system-level
solutions based... | 2,013 |
Fast Reinforcement Learning for Energy-Efficient Wireless Communications | 2,013 |
To classify time series by nearest neighbors, we need to specify or learn one
or several distance measures. We consider variations of the Mahalanobis
distance measures which rely on the inverse covariance matrix of the data.
Unfortunately --- for time series data --- the covariance matrix has often low
rank. To allev... | 2,012 |
Time Series Classification by Class-Specific Mahalanobis Distance
Measures | 2,012 |
We present a simple and fast geometric method for modeling data by a union of
affine subspaces. The method begins by forming a collection of local best-fit
affine subspaces, i.e., subspaces approximating the data in local
neighborhoods. The correct sizes of the local neighborhoods are determined
automatically by the ... | 2,012 |
Hybrid Linear Modeling via Local Best-fit Flats | 2,012 |
A scattering vector is a local descriptor including multiscale and
multi-direction co-occurrence information. It is computed with a cascade of
wavelet decompositions and complex modulus. This scattering representation is
locally translation invariant and linearizes deformations. A supervised
classification algorithm ... | 2,013 |
Classification with Scattering Operators | 2,013 |
We obtain a tight distribution-specific characterization of the sample
complexity of large-margin classification with L_2 regularization: We introduce
the \gamma-adapted-dimension, which is a simple function of the spectrum of a
distribution's covariance matrix, and show distribution-specific upper and
lower bounds o... | 2,012 |
Tight Sample Complexity of Large-Margin Learning | 2,012 |
Many popular Bayesian nonparametric priors can be characterized in terms of
exchangeable species sampling sequences. However, in some applications,
exchangeability may not be appropriate. We introduce a {novel and
probabilistically coherent family of non-exchangeable species sampling
sequences characterized by a trac... | 2,014 |
Generalized Species Sampling Priors with Latent Beta reinforcements | 2,014 |
We assume data sampled from a mixture of d-dimensional linear subspaces with
spherically symmetric distributions within each subspace and an additional
outlier component with spherically symmetric distribution within the ambient
space (for simplicity we may assume that all distributions are uniform on their
correspon... | 2,014 |
lp-Recovery of the Most Significant Subspace among Multiple Subspaces
with Outliers | 2,014 |
We consider the problem of online linear regression on arbitrary
deterministic sequences when the ambient dimension d can be much larger than
the number of time rounds T. We introduce the notion of sparsity regret bound,
which is a deterministic online counterpart of recent risk bounds derived in
the stochastic setti... | 2,013 |
Sparsity regret bounds for individual sequences in online linear
regression | 2,013 |
This paper constructs translation invariant operators on L2(R^d), which are
Lipschitz continuous to the action of diffeomorphisms. A scattering propagator
is a path ordered product of non-linear and non-commuting operators, each of
which computes the modulus of a wavelet transform. A local integration defines
a windo... | 2,012 |
Group Invariant Scattering | 2,012 |
This work is motivated by the problem of image mis-registration in remote
sensing and we are interested in determining the resulting loss in the accuracy
of pattern classification. A statistical formulation is given where we propose
to use data contamination to model and understand the phenomenon of image
mis-registr... | 2,012 |
Classification under Data Contamination with Application to Remote
Sensing Image Mis-registration | 2,012 |
Targeting at sparse learning, we construct Banach spaces B of functions on an
input space X with the properties that (1) B possesses an l1 norm in the sense
that it is isometrically isomorphic to the Banach space of integrable functions
on X with respect to the counting measure; (2) point evaluations are continuous
l... | 2,012 |
Reproducing Kernel Banach Spaces with the l1 Norm | 2,012 |
We consider a retailer selling a single product with limited on-hand
inventory over a finite selling season. Customer demand arrives according to a
Poisson process, the rate of which is influenced by a single action taken by
the retailer (such as price adjustment, sales commission, advertisement
intensity, etc.). The... | 2,013 |
Close the Gaps: A Learning-while-Doing Algorithm for a Class of
Single-Product Revenue Management Problems | 2,013 |
Boosting combines weak learners into a predictor with low empirical risk. Its
dual constructs a high entropy distribution upon which weak learners and
training labels are uncorrelated. This manuscript studies this primal-dual
relationship under a broad family of losses, including the exponential loss of
AdaBoost and ... | 2,012 |
A Primal-Dual Convergence Analysis of Boosting | 2,012 |
We reformulate minimalist grammars as partial functions on term algebras for
strings and trees. Using filler/role bindings and tensor product
representations, we construct homomorphisms for these data structures into
geometric vector spaces. We prove that the structure-building functions as well
as simple processors ... | 2,012 |
Geometric representations for minimalist grammars | 2,012 |
Prediction markets are used in real life to predict outcomes of interest such
as presidential elections. This paper presents a mathematical theory of
artificial prediction markets for supervised learning of conditional
probability estimators. The artificial prediction market is a novel method for
fusing the predictio... | 2,012 |
An Introduction to Artificial Prediction Markets for Classification | 2,012 |
Ordinal regression is commonly formulated as a multi-class problem with
ordinal constraints. The challenge of designing accurate classifiers for
ordinal regression generally increases with the number of classes involved, due
to the large number of labeled patterns that are needed. The availability of
ordinal class la... | 2,012 |
Transductive Ordinal Regression | 2,012 |
Feature selection with specific multivariate performance measures is the key
to the success of many applications, such as image retrieval and text
classification. The existing feature selection methods are usually designed for
classification error. In this paper, we propose a generalized sparse
regularizer. Based on ... | 2,013 |
A Feature Selection Method for Multivariate Performance Measures | 2,013 |
In the 21st century, Aerial and satellite images are information rich. They
are also complex to analyze. For GIS systems, many features require fast and
reliable extraction of open space area from high resolution satellite imagery.
In this paper we will study efficient and reliable automatic extraction
algorithm to f... | 2,022 |
Automatic Extraction of Open Space Area from High Resolution Urban
Satellite Imagery | 2,022 |
In many practical applications of clustering, the objects to be clustered
evolve over time, and a clustering result is desired at each time step. In such
applications, evolutionary clustering typically outperforms traditional static
clustering by producing clustering results that reflect long-term trends while
being ... | 2,013 |
Adaptive Evolutionary Clustering | 2,013 |
With inspiration from Random Forests (RF) in the context of classification, a
new clustering ensemble method---Cluster Forests (CF) is proposed.
Geometrically, CF randomly probes a high-dimensional data cloud to obtain "good
local clusterings" and then aggregates via spectral clustering to obtain
cluster assignments ... | 2,013 |
Cluster Forests | 2,013 |
Gabor filters play an important role in many application areas for the
enhancement of various types of images and the extraction of Gabor features.
For the purpose of enhancing curved structures in noisy images, we introduce
curved Gabor filters which locally adapt their shape to the direction of flow.
These curved G... | 2,014 |
Curved Gabor Filters for Fingerprint Image Enhancement | 2,014 |
The success of many machine learning and pattern recognition methods relies
heavily upon the identification of an appropriate distance metric on the input
data. It is often beneficial to learn such a metric from the input training
data, instead of using a default one such as the Euclidean distance. In this
work, we p... | 2,012 |
Positive Semidefinite Metric Learning Using Boosting-like Algorithms | 2,012 |
This paper considers the problem of clustering a partially observed
unweighted graph---i.e., one where for some node pairs we know there is an edge
between them, for some others we know there is no edge, and for the remaining
we do not know whether or not there is an edge. We want to organize the nodes
into disjoint ... | 2,014 |
Clustering Partially Observed Graphs via Convex Optimization | 2,014 |
We present a new application and covering number bound for the framework of
"Machine Learning with Operational Costs (MLOC)," which is an exploratory form
of decision theory. The MLOC framework incorporates knowledge about how a
predictive model will be used for a subsequent task, thus combining machine
learning with... | 2,014 |
On Combining Machine Learning with Decision Making | 2,014 |
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