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4,500 | Competitive on-line learning with a convex loss function | cs.LG | We consider the problem of sequential decision making under uncertainty in
which the loss caused by a decision depends on the following binary
observation. In competitive on-line learning, the goal is to design decision
algorithms that are almost as good as the best decision rules in a wide
benchmark class, without mak... | computer science |
4,501 | Universal Learning of Repeated Matrix Games | cs.LG | We study and compare the learning dynamics of two universal learning
algorithms, one based on Bayesian learning and the other on prediction with
expert advice. Both approaches have strong asymptotic performance guarantees.
When confronted with the task of finding good long-term strategies in repeated
2x2 matrix games, ... | computer science |
4,502 | When Ignorance is Bliss | cs.AI | It is commonly-accepted wisdom that more information is better, and that
information should never be ignored. Here we argue, using both a Bayesian and a
non-Bayesian analysis, that in some situations you are better off ignoring
information if your uncertainty is represented by a set of probability
measures. These inclu... | computer science |
4,503 | Identifying Interaction Sites in "Recalcitrant" Proteins: Predicted
Protein and Rna Binding Sites in Rev Proteins of Hiv-1 and Eiav Agree with
Experimental Data | cs.LG | Protein-protein and protein nucleic acid interactions are vitally important
for a wide range of biological processes, including regulation of gene
expression, protein synthesis, and replication and assembly of many viruses. We
have developed machine learning approaches for predicting which amino acids of
a protein part... | computer science |
4,504 | Asymptotic Learnability of Reinforcement Problems with Arbitrary
Dependence | cs.LG | We address the problem of reinforcement learning in which observations may
exhibit an arbitrary form of stochastic dependence on past observations and
actions. The task for an agent is to attain the best possible asymptotic reward
where the true generating environment is unknown but belongs to a known
countable family ... | computer science |
4,505 | Nearly optimal exploration-exploitation decision thresholds | cs.AI | While in general trading off exploration and exploitation in reinforcement
learning is hard, under some formulations relatively simple solutions exist.
Optimal decision thresholds for the multi-armed bandit problem, one for the
infinite horizon discounted reward case and one for the finite horizon
undiscounted reward c... | computer science |
4,506 | A Formal Measure of Machine Intelligence | cs.AI | A fundamental problem in artificial intelligence is that nobody really knows
what intelligence is. The problem is especially acute when we need to consider
artificial systems which are significantly different to humans. In this paper
we approach this problem in the following way: We take a number of well known
informal... | computer science |
4,507 | A Foundation to Perception Computing, Logic and Automata | cs.AI | In this report, a novel approach to intelligence and learning is introduced,
this approach is based on what we call 'perception logic'. Based on this logic,
a computing mechanism and automata are introduced. Multi-resolution analysis of
perceptual information is given, in which learning is accomplished in at most
O(log... | computer science |
4,508 | Player co-modelling in a strategy board game: discovering how to play
fast | cs.AI | In this paper we experiment with a 2-player strategy board game where playing
models are evolved using reinforcement learning and neural networks. The models
are evolved to speed up automatic game development based on human involvement
at varying levels of sophistication and density when compared to fully
autonomous pl... | computer science |
4,509 | Universal Algorithmic Intelligence: A mathematical top->down approach | cs.AI | Sequential decision theory formally solves the problem of rational agents in
uncertain worlds if the true environmental prior probability distribution is
known. Solomonoff's theory of universal induction formally solves the problem
of sequence prediction for unknown prior distribution. We combine both ideas
and get a p... | computer science |
4,510 | On the monotonization of the training set | cs.LG | We consider the problem of minimal correction of the training set to make it
consistent with monotonic constraints. This problem arises during analysis of
data sets via techniques that require monotone data. We show that this problem
is NP-hard in general and is equivalent to finding a maximal independent set in
specia... | computer science |
4,511 | Loop corrections for message passing algorithms in continuous variable
models | cs.AI | In this paper we derive the equations for Loop Corrected Belief Propagation
on a continuous variable Gaussian model. Using the exactness of the averages
for belief propagation for Gaussian models, a different way of obtaining the
covariances is found, based on Belief Propagation on cavity graphs. We discuss
the relatio... | computer science |
4,512 | A Novel Model of Working Set Selection for SMO Decomposition Methods | cs.LG | In the process of training Support Vector Machines (SVMs) by decomposition
methods, working set selection is an important technique, and some exciting
schemes were employed into this field. To improve working set selection, we
propose a new model for working set selection in sequential minimal
optimization (SMO) decomp... | computer science |
4,513 | Model Selection Through Sparse Maximum Likelihood Estimation | cs.AI | We consider the problem of estimating the parameters of a Gaussian or binary
distribution in such a way that the resulting undirected graphical model is
sparse. Our approach is to solve a maximum likelihood problem with an added
l_1-norm penalty term. The problem as formulated is convex but the memory
requirements and ... | computer science |
4,514 | Optimal Solutions for Sparse Principal Component Analysis | cs.AI | Given a sample covariance matrix, we examine the problem of maximizing the
variance explained by a linear combination of the input variables while
constraining the number of nonzero coefficients in this combination. This is
known as sparse principal component analysis and has a wide array of
applications in machine lea... | computer science |
4,515 | Clusters, Graphs, and Networks for Analysing Internet-Web-Supported
Communication within a Virtual Community | cs.AI | The proposal is to use clusters, graphs and networks as models in order to
analyse the Web structure. Clusters, graphs and networks provide knowledge
representation and organization. Clusters were generated by co-site analysis.
The sample is a set of academic Web sites from the countries belonging to the
European Union... | computer science |
4,516 | Classification of Images Using Support Vector Machines | cs.LG | Support Vector Machines (SVMs) are a relatively new supervised classification
technique to the land cover mapping community. They have their roots in
Statistical Learning Theory and have gained prominence because they are robust,
accurate and are effective even when using a small training sample. By their
nature SVMs a... | computer science |
4,517 | Factored Value Iteration Converges | cs.AI | In this paper we propose a novel algorithm, factored value iteration (FVI),
for the approximate solution of factored Markov decision processes (fMDPs). The
traditional approximate value iteration algorithm is modified in two ways. For
one, the least-squares projection operator is modified so that it does not
increase m... | computer science |
4,518 | Support Vector Machine Classification with Indefinite Kernels | cs.LG | We propose a method for support vector machine classification using
indefinite kernels. Instead of directly minimizing or stabilizing a nonconvex
loss function, our algorithm simultaneously computes support vectors and a
proxy kernel matrix used in forming the loss. This can be interpreted as a
penalized kernel learnin... | computer science |
4,519 | A Unified Semi-Supervised Dimensionality Reduction Framework for
Manifold Learning | cs.LG | We present a general framework of semi-supervised dimensionality reduction
for manifold learning which naturally generalizes existing supervised and
unsupervised learning frameworks which apply the spectral decomposition.
Algorithms derived under our framework are able to employ both labeled and
unlabeled examples and ... | computer science |
4,520 | On Kernelization of Supervised Mahalanobis Distance Learners | cs.LG | This paper focuses on the problem of kernelizing an existing supervised
Mahalanobis distance learner. The following features are included in the paper.
Firstly, three popular learners, namely, "neighborhood component analysis",
"large margin nearest neighbors" and "discriminant neighborhood embedding",
which do not hav... | computer science |
4,521 | A Kernel Method for the Two-Sample Problem | cs.LG | We propose a framework for analyzing and comparing distributions, allowing us
to design statistical tests to determine if two samples are drawn from
different distributions. Our test statistic is the largest difference in
expectations over functions in the unit ball of a reproducing kernel Hilbert
space (RKHS). We pres... | computer science |
4,522 | Learning Low-Density Separators | cs.LG | We define a novel, basic, unsupervised learning problem - learning the lowest
density homogeneous hyperplane separator of an unknown probability
distribution. This task is relevant to several problems in machine learning,
such as semi-supervised learning and clustering stability. We investigate the
question of existenc... | computer science |
4,523 | On Sequences with Non-Learnable Subsequences | cs.AI | The remarkable results of Foster and Vohra was a starting point for a series
of papers which show that any sequence of outcomes can be learned (with no
prior knowledge) using some universal randomized forecasting algorithm and
forecast-dependent checking rules. We show that for the class of all
computationally efficien... | computer science |
4,524 | Prediction with Expert Advice in Games with Unbounded One-Step Gains | cs.LG | The games of prediction with expert advice are considered in this paper. We
present some modification of Kalai and Vempala algorithm of following the
perturbed leader for the case of unrestrictedly large one-step gains. We show
that in general case the cumulative gain of any probabilistic prediction
algorithm can be mu... | computer science |
4,525 | On empirical meaning of randomness with respect to a real parameter | cs.LG | We study the empirical meaning of randomness with respect to a family of
probability distributions $P_\theta$, where $\theta$ is a real parameter, using
algorithmic randomness theory. In the case when for a computable probability
distribution $P_\theta$ an effectively strongly consistent estimate exists, we
show that t... | computer science |
4,526 | Sparse Online Learning via Truncated Gradient | cs.LG | We propose a general method called truncated gradient to induce sparsity in
the weights of online learning algorithms with convex loss functions. This
method has several essential properties: The degree of sparsity is continuous
-- a parameter controls the rate of sparsification from no sparsification to
total sparsifi... | computer science |
4,527 | Multi-Instance Learning by Treating Instances As Non-I.I.D. Samples | cs.LG | Multi-instance learning attempts to learn from a training set consisting of
labeled bags each containing many unlabeled instances. Previous studies
typically treat the instances in the bags as independently and identically
distributed. However, the instances in a bag are rarely independent, and
therefore a better perfo... | computer science |
4,528 | Predicting Abnormal Returns From News Using Text Classification | cs.LG | We show how text from news articles can be used to predict intraday price
movements of financial assets using support vector machines. Multiple kernel
learning is used to combine equity returns with text as predictive features to
increase classification performance and we develop an analytic center cutting
plane method... | computer science |
4,529 | A Spectral Algorithm for Learning Hidden Markov Models | cs.LG | Hidden Markov Models (HMMs) are one of the most fundamental and widely used
statistical tools for modeling discrete time series. In general, learning HMMs
from data is computationally hard (under cryptographic assumptions), and
practitioners typically resort to search heuristics which suffer from the usual
local optima... | computer science |
4,530 | Learning Class-Level Bayes Nets for Relational Data | cs.LG | Many databases store data in relational format, with different types of
entities and information about links between the entities. The field of
statistical-relational learning (SRL) has developed a number of new statistical
models for such data. In this paper we focus on learning class-level or
first-order dependencies... | computer science |
4,531 | Discovering general partial orders in event streams | cs.AI | Frequent episode discovery is a popular framework for pattern discovery in
event streams. An episode is a partially ordered set of nodes with each node
associated with an event type. Efficient (and separate) algorithms exist for
episode discovery when the associated partial order is total (serial episode)
and trivial (... | computer science |
4,532 | Error-Correcting Tournaments | cs.AI | We present a family of pairwise tournaments reducing $k$-class classification
to binary classification. These reductions are provably robust against a
constant fraction of binary errors. The results improve on the PECOC
construction \cite{SECOC} with an exponential improvement in computation, from
$O(k)$ to $O(\log_2 k... | computer science |
4,533 | Domain Adaptation: Learning Bounds and Algorithms | cs.LG | This paper addresses the general problem of domain adaptation which arises in
a variety of applications where the distribution of the labeled sample
available somewhat differs from that of the test data. Building on previous
work by Ben-David et al. (2007), we introduce a novel distance between
distributions, discrepan... | computer science |
4,534 | Performing Nonlinear Blind Source Separation with Signal Invariants | cs.AI | Given a time series of multicomponent measurements x(t), the usual objective
of nonlinear blind source separation (BSS) is to find a "source" time series
s(t), comprised of statistically independent combinations of the measured
components. In this paper, the source time series is required to have a density
function in ... | computer science |
4,535 | Online prediction of ovarian cancer | cs.AI | In this paper we apply computer learning methods to diagnosing ovarian cancer
using the level of the standard biomarker CA125 in conjunction with information
provided by mass-spectrometry. We are working with a new data set collected
over a period of 7 years. Using the level of CA125 and mass-spectrometry peaks,
our al... | computer science |
4,536 | A Methodology for Learning Players' Styles from Game Records | cs.AI | We describe a preliminary investigation into learning a Chess player's style
from game records. The method is based on attempting to learn features of a
player's individual evaluation function using the method of temporal
differences, with the aid of a conventional Chess engine architecture. Some
encouraging results we... | computer science |
4,537 | Exponential Family Graph Matching and Ranking | cs.LG | We present a method for learning max-weight matching predictors in bipartite
graphs. The method consists of performing maximum a posteriori estimation in
exponential families with sufficient statistics that encode permutations and
data features. Although inference is in general hard, we show that for one very
relevant ... | computer science |
4,538 | Optimistic Initialization and Greediness Lead to Polynomial Time
Learning in Factored MDPs - Extended Version | cs.AI | In this paper we propose an algorithm for polynomial-time reinforcement
learning in factored Markov decision processes (FMDPs). The factored optimistic
initial model (FOIM) algorithm, maintains an empirical model of the FMDP in a
conventional way, and always follows a greedy policy with respect to its model.
The only t... | computer science |
4,539 | Considerations upon the Machine Learning Technologies | cs.LG | Artificial intelligence offers superior techniques and methods by which
problems from diverse domains may find an optimal solution. The Machine
Learning technologies refer to the domain of artificial intelligence aiming to
develop the techniques allowing the computers to "learn". Some systems based on
Machine Learning ... | computer science |
4,540 | Learning Nonlinear Dynamic Models | cs.AI | We present a novel approach for learning nonlinear dynamic models, which
leads to a new set of tools capable of solving problems that are otherwise
difficult. We provide theory showing this new approach is consistent for models
with long range structure, and apply the approach to motion capture and
high-dimensional vid... | computer science |
4,541 | ABC-LogitBoost for Multi-class Classification | cs.LG | We develop abc-logitboost, based on the prior work on abc-boost and robust
logitboost. Our extensive experiments on a variety of datasets demonstrate the
considerable improvement of abc-logitboost over logitboost and abc-mart. | computer science |
4,542 | Feature-Weighted Linear Stacking | cs.LG | Ensemble methods, such as stacking, are designed to boost predictive accuracy
by blending the predictions of multiple machine learning models. Recent work
has shown that the use of meta-features, additional inputs describing each
example in a dataset, can boost the performance of ensemble methods, but the
greatest repo... | computer science |
4,543 | Machine Learning: When and Where the Horses Went Astray? | cs.AI | Machine Learning is usually defined as a subfield of AI, which is busy with
information extraction from raw data sets. Despite of its common acceptance and
widespread recognition, this definition is wrong and groundless. Meaningful
information does not belong to the data that bear it. It belongs to the
observers of the... | computer science |
4,544 | A Bayesian Rule for Adaptive Control based on Causal Interventions | cs.AI | Explaining adaptive behavior is a central problem in artificial intelligence
research. Here we formalize adaptive agents as mixture distributions over
sequences of inputs and outputs (I/O). Each distribution of the mixture
constitutes a `possible world', but the agent does not know which of the
possible worlds it is ac... | computer science |
4,545 | Closing the Learning-Planning Loop with Predictive State Representations | cs.LG | A central problem in artificial intelligence is that of planning to maximize
future reward under uncertainty in a partially observable environment. In this
paper we propose and demonstrate a novel algorithm which accurately learns a
model of such an environment directly from sequences of action-observation
pairs. We th... | computer science |
4,546 | Learning to Predict Combinatorial Structures | cs.LG | The major challenge in designing a discriminative learning algorithm for
predicting structured data is to address the computational issues arising from
the exponential size of the output space. Existing algorithms make different
assumptions to ensure efficient, polynomial time estimation of model
parameters. For severa... | computer science |
4,547 | Complexity of stochastic branch and bound methods for belief tree search
in Bayesian reinforcement learning | cs.LG | There has been a lot of recent work on Bayesian methods for reinforcement
learning exhibiting near-optimal online performance. The main obstacle facing
such methods is that in most problems of interest, the optimal solution
involves planning in an infinitely large tree. However, it is possible to
obtain stochastic lowe... | computer science |
4,548 | Convergence of Bayesian Control Rule | cs.AI | Recently, new approaches to adaptive control have sought to reformulate the
problem as a minimization of a relative entropy criterion to obtain tractable
solutions. In particular, it has been shown that minimizing the expected
deviation from the causal input-output dependencies of the true plant leads to
a new promisin... | computer science |
4,549 | A new approach to content-based file type detection | cs.LG | File type identification and file type clustering may be difficult tasks that
have an increasingly importance in the field of computer and network security.
Classical methods of file type detection including considering file extensions
and magic bytes can be easily spoofed. Content-based file type detection is a
newer ... | computer science |
4,550 | Less Regret via Online Conditioning | cs.LG | We analyze and evaluate an online gradient descent algorithm with adaptive
per-coordinate adjustment of learning rates. Our algorithm can be thought of as
an online version of batch gradient descent with a diagonal preconditioner.
This approach leads to regret bounds that are stronger than those of standard
online grad... | computer science |
4,551 | Ontology-supported processing of clinical text using medical knowledge
integration for multi-label classification of diagnosis coding | cs.LG | This paper discusses the knowledge integration of clinical information
extracted from distributed medical ontology in order to ameliorate a machine
learning-based multi-label coding assignment system. The proposed approach is
implemented using a decision tree based cascade hierarchical technique on the
university hospi... | computer science |
4,552 | Adaptive Bases for Reinforcement Learning | cs.LG | We consider the problem of reinforcement learning using function
approximation, where the approximating basis can change dynamically while
interacting with the environment. A motivation for such an approach is
maximizing the value function fitness to the problem faced. Three errors are
considered: approximation square ... | computer science |
4,553 | Approximated Structured Prediction for Learning Large Scale Graphical
Models | cs.LG | This manuscripts contains the proofs for "A Primal-Dual Message-Passing
Algorithm for Approximated Large Scale Structured Prediction". | computer science |
4,554 | Distributed Autonomous Online Learning: Regrets and Intrinsic
Privacy-Preserving Properties | cs.LG | Online learning has become increasingly popular on handling massive data. The
sequential nature of online learning, however, requires a centralized learner
to store data and update parameters. In this paper, we consider online learning
with {\em distributed} data sources. The autonomous learners update local
parameters... | computer science |
4,555 | Feature Construction for Relational Sequence Learning | cs.AI | We tackle the problem of multi-class relational sequence learning using
relevant patterns discovered from a set of labelled sequences. To deal with
this problem, firstly each relational sequence is mapped into a feature vector
using the result of a feature construction method. Since, the efficacy of
sequence learning a... | computer science |
4,556 | Gaussian Process Bandits for Tree Search: Theory and Application to
Planning in Discounted MDPs | cs.LG | We motivate and analyse a new Tree Search algorithm, GPTS, based on recent
theoretical advances in the use of Gaussian Processes for Bandit problems. We
consider tree paths as arms and we assume the target/reward function is drawn
from a GP distribution. The posterior mean and variance, after observing data,
are used t... | computer science |
4,557 | Predictive State Temporal Difference Learning | cs.LG | We propose a new approach to value function approximation which combines
linear temporal difference reinforcement learning with subspace identification.
In practical applications, reinforcement learning (RL) is complicated by the
fact that state is either high-dimensional or partially observable. Therefore,
RL methods ... | computer science |
4,558 | Efficient Optimization of Performance Measures by Classifier Adaptation | cs.LG | In practical applications, machine learning algorithms are often needed to
learn classifiers that optimize domain specific performance measures.
Previously, the research has focused on learning the needed classifier in
isolation, yet learning nonlinear classifier for nonlinear and nonsmooth
performance measures is stil... | computer science |
4,559 | Inverse-Category-Frequency based supervised term weighting scheme for
text categorization | cs.LG | Term weighting schemes often dominate the performance of many classifiers,
such as kNN, centroid-based classifier and SVMs. The widely used term weighting
scheme in text categorization, i.e., tf.idf, is originated from information
retrieval (IR) field. The intuition behind idf for text categorization seems
less reasona... | computer science |
4,560 | From Machine Learning to Machine Reasoning | cs.AI | A plausible definition of "reasoning" could be "algebraically manipulating
previously acquired knowledge in order to answer a new question". This
definition covers first-order logical inference or probabilistic inference. It
also includes much simpler manipulations commonly used to build large learning
systems. For ins... | computer science |
4,561 | Decision Making Agent Searching for Markov Models in Near-Deterministic
World | cs.AI | Reinforcement learning has solid foundations, but becomes inefficient in
partially observed (non-Markovian) environments. Thus, a learning agent -born
with a representation and a policy- might wish to investigate to what extent
the Markov property holds. We propose a learning architecture that utilizes
combinatorial po... | computer science |
4,562 | Semantic Vector Machines | cs.LG | We first present our work in machine translation, during which we used
aligned sentences to train a neural network to embed n-grams of different
languages into an $d$-dimensional space, such that n-grams that are the
translation of each other are close with respect to some metric. Good n-grams
to n-grams translation re... | computer science |
4,563 | Feature Selection for MAUC-Oriented Classification Systems | cs.LG | Feature selection is an important pre-processing step for many pattern
classification tasks. Traditionally, feature selection methods are designed to
obtain a feature subset that can lead to high classification accuracy. However,
classification accuracy has recently been shown to be an inappropriate
performance metric ... | computer science |
4,564 | Learning to Order Things | cs.LG | There are many applications in which it is desirable to order rather than
classify instances. Here we consider the problem of learning how to order
instances given feedback in the form of preference judgments, i.e., statements
to the effect that one instance should be ranked ahead of another. We outline a
two-stage app... | computer science |
4,565 | Learning unbelievable marginal probabilities | cs.AI | Loopy belief propagation performs approximate inference on graphical models
with loops. One might hope to compensate for the approximation by adjusting
model parameters. Learning algorithms for this purpose have been explored
previously, and the claim has been made that every set of locally consistent
marginals can ari... | computer science |
4,566 | Optimizing Dialogue Management with Reinforcement Learning: Experiments
with the NJFun System | cs.LG | Designing the dialogue policy of a spoken dialogue system involves many
nontrivial choices. This paper presents a reinforcement learning approach for
automatically optimizing a dialogue policy, which addresses the technical
challenges in applying reinforcement learning to a working dialogue system with
human users. We ... | computer science |
4,567 | Accelerating Reinforcement Learning through Implicit Imitation | cs.LG | Imitation can be viewed as a means of enhancing learning in multiagent
environments. It augments an agent's ability to learn useful behaviors by
making intelligent use of the knowledge implicit in behaviors demonstrated by
cooperative teachers or other more experienced agents. We propose and study a
formal model of imp... | computer science |
4,568 | Efficient Reinforcement Learning Using Recursive Least-Squares Methods | cs.LG | The recursive least-squares (RLS) algorithm is one of the most well-known
algorithms used in adaptive filtering, system identification and adaptive
control. Its popularity is mainly due to its fast convergence speed, which is
considered to be optimal in practice. In this paper, RLS methods are used to
solve reinforceme... | computer science |
4,569 | Specific-to-General Learning for Temporal Events with Application to
Learning Event Definitions from Video | cs.AI | We develop, analyze, and evaluate a novel, supervised, specific-to-general
learner for a simple temporal logic and use the resulting algorithm to learn
visual event definitions from video sequences. First, we introduce a simple,
propositional, temporal, event-description language called AMA that is
sufficiently express... | computer science |
4,570 | CBR with Commonsense Reasoning and Structure Mapping: An Application to
Mediation | cs.AI | Mediation is an important method in dispute resolution. We implement a case
based reasoning approach to mediation integrating analogical and commonsense
reasoning components that allow an artificial mediation agent to satisfy
requirements expected from a human mediator, in particular: utilizing
experience with cases in... | computer science |
4,571 | Uncertain Nearest Neighbor Classification | cs.LG | This work deals with the problem of classifying uncertain data. With this aim
the Uncertain Nearest Neighbor (UNN) rule is here introduced, which represents
the generalization of the deterministic nearest neighbor rule to the case in
which uncertain objects are available. The UNN rule relies on the concept of
nearest n... | computer science |
4,572 | A survey on independence-based Markov networks learning | cs.AI | This work reports the most relevant technical aspects in the problem of
learning the \emph{Markov network structure} from data. Such problem has become
increasingly important in machine learning, and many other application fields
of machine learning. Markov networks, together with Bayesian networks, are
probabilistic g... | computer science |
4,573 | Premise Selection for Mathematics by Corpus Analysis and Kernel Methods | cs.LG | Smart premise selection is essential when using automated reasoning as a tool
for large-theory formal proof development. A good method for premise selection
in complex mathematical libraries is the application of machine learning to
large corpora of proofs. This work develops learning-based premise selection in
two way... | computer science |
4,574 | Datum-Wise Classification: A Sequential Approach to Sparsity | cs.AI | We propose a novel classification technique whose aim is to select an
appropriate representation for each datapoint, in contrast to the usual
approach of selecting a representation encompassing the whole dataset. This
datum-wise representation is found by using a sparsity inducing empirical risk,
which is a relaxation ... | computer science |
4,575 | Transfer from Multiple MDPs | cs.AI | Transfer reinforcement learning (RL) methods leverage on the experience
collected on a set of source tasks to speed-up RL algorithms. A simple and
effective approach is to transfer samples from source tasks and include them
into the training set used to solve a given target task. In this paper, we
investigate the theor... | computer science |
4,576 | Two Projection Pursuit Algorithms for Machine Learning under
Non-Stationarity | cs.LG | This thesis derives, tests and applies two linear projection algorithms for
machine learning under non-stationarity. The first finds a direction in a
linear space upon which a data set is maximally non-stationary. The second aims
to robustify two-way classification against non-stationarity. The algorithm is
tested on a... | computer science |
4,577 | Learning Symbolic Models of Stochastic Domains | cs.LG | In this article, we work towards the goal of developing agents that can learn
to act in complex worlds. We develop a probabilistic, relational planning rule
representation that compactly models noisy, nondeterministic action effects,
and show how such rules can be effectively learned. Through experiments in
simple plan... | computer science |
4,578 | Inducing Probabilistic Programs by Bayesian Program Merging | cs.AI | This report outlines an approach to learning generative models from data. We
express models as probabilistic programs, which allows us to capture abstract
patterns within the examples. By choosing our language for programs to be an
extension of the algebraic data type of the examples, we can begin with a
program that g... | computer science |
4,579 | Approximate Stochastic Subgradient Estimation Training for Support
Vector Machines | cs.LG | Subgradient algorithms for training support vector machines have been quite
successful for solving large-scale and online learning problems. However, they
have been restricted to linear kernels and strongly convex formulations. This
paper describes efficient subgradient approaches without such limitations. Our
approach... | computer science |
4,580 | Online Learning with Preference Feedback | cs.LG | We propose a new online learning model for learning with preference feedback.
The model is especially suited for applications like web search and recommender
systems, where preference data is readily available from implicit user feedback
(e.g. clicks). In particular, at each time step a potentially structured object
(e... | computer science |
4,581 | A Bayesian Model for Plan Recognition in RTS Games applied to StarCraft | cs.LG | The task of keyhole (unobtrusive) plan recognition is central to adaptive
game AI. "Tech trees" or "build trees" are the core of real-time strategy (RTS)
game strategic (long term) planning. This paper presents a generic and simple
Bayesian model for RTS build tree prediction from noisy observations, which
parameters a... | computer science |
4,582 | A probabilistic methodology for multilabel classification | cs.AI | Multilabel classification is a relatively recent subfield of machine
learning. Unlike to the classical approach, where instances are labeled with
only one category, in multilabel classification, an arbitrary number of
categories is chosen to label an instance. Due to the problem complexity (the
solution is one among an... | computer science |
4,583 | Unsupervised Classification Using Immune Algorithm | cs.LG | Unsupervised classification algorithm based on clonal selection principle
named Unsupervised Clonal Selection Classification (UCSC) is proposed in this
paper. The new proposed algorithm is data driven and self-adaptive, it adjusts
its parameters to the data to make the classification operation as fast as
possible. The ... | computer science |
4,584 | Empowerment for Continuous Agent-Environment Systems | cs.AI | This paper develops generalizations of empowerment to continuous states.
Empowerment is a recently introduced information-theoretic quantity motivated
by hypotheses about the efficiency of the sensorimotor loop in biological
organisms, but also from considerations stemming from curiosity-driven
learning. Empowemerment ... | computer science |
4,585 | Gaussian Processes for Sample Efficient Reinforcement Learning with
RMAX-like Exploration | cs.AI | We present an implementation of model-based online reinforcement learning
(RL) for continuous domains with deterministic transitions that is specifically
designed to achieve low sample complexity. To achieve low sample complexity,
since the environment is unknown, an agent must intelligently balance
exploration and exp... | computer science |
4,586 | Feature Selection for Value Function Approximation Using Bayesian Model
Selection | cs.AI | Feature selection in reinforcement learning (RL), i.e. choosing basis
functions such that useful approximations of the unkown value function can be
obtained, is one of the main challenges in scaling RL to real-world
applications. Here we consider the Gaussian process based framework GPTD for
approximate policy evaluati... | computer science |
4,587 | Algorithms for Learning Kernels Based on Centered Alignment | cs.LG | This paper presents new and effective algorithms for learning kernels. In
particular, as shown by our empirical results, these algorithms consistently
outperform the so-called uniform combination solution that has proven to be
difficult to improve upon in the past, as well as other algorithms for learning
kernels based... | computer science |
4,588 | Evolving Culture vs Local Minima | cs.LG | We propose a theory that relates difficulty of learning in deep architectures
to culture and language. It is articulated around the following hypotheses: (1)
learning in an individual human brain is hampered by the presence of effective
local minima; (2) this optimization difficulty is particularly important when
it co... | computer science |
4,589 | Knapsack based Optimal Policies for Budget-Limited Multi-Armed Bandits | cs.AI | In budget-limited multi-armed bandit (MAB) problems, the learner's actions
are costly and constrained by a fixed budget. Consequently, an optimal
exploitation policy may not be to pull the optimal arm repeatedly, as is the
case in other variants of MAB, but rather to pull the sequence of different
arms that maximises t... | computer science |
4,590 | Knowledge revision in systems based on an informed tree search strategy
: application to cartographic generalisation | cs.AI | Many real world problems can be expressed as optimisation problems. Solving
this kind of problems means to find, among all possible solutions, the one that
maximises an evaluation function. One approach to solve this kind of problem is
to use an informed search strategy. The principle of this kind of strategy is
to use... | computer science |
4,591 | Evolution and the structure of learning agents | cs.AI | This paper presents the thesis that all learning agents of finite information
size are limited by their informational structure in what goals they can
efficiently learn to achieve in a complex environment. Evolutionary change is
critical for creating the required structure for all learning agents in any
complex environ... | computer science |
4,592 | On Move Pattern Trends in a Large Go Games Corpus | cs.AI | We process a large corpus of game records of the board game of Go and propose
a way of extracting summary information on played moves. We then apply several
basic data-mining methods on the summary information to identify the most
differentiating features within the summary information, and discuss their
correspondence... | computer science |
4,593 | Feature selection with test cost constraint | cs.AI | Feature selection is an important preprocessing step in machine learning and
data mining. In real-world applications, costs, including money, time and other
resources, are required to acquire the features. In some cases, there is a test
cost constraint due to limited resources. We shall deliberately select an
informati... | computer science |
4,594 | The Thing That We Tried Didn't Work Very Well : Deictic Representation
in Reinforcement Learning | cs.LG | Most reinforcement learning methods operate on propositional representations
of the world state. Such representations are often intractably large and
generalize poorly. Using a deictic representation is believed to be a viable
alternative: they promise generalization while allowing the use of existing
reinforcement-lea... | computer science |
4,595 | Asymptotic Model Selection for Naive Bayesian Networks | cs.AI | We develop a closed form asymptotic formula to compute the marginal
likelihood of data given a naive Bayesian network model with two hidden states
and binary features. This formula deviates from the standard BIC score. Our
work provides a concrete example that the BIC score is generally not valid for
statistical models... | computer science |
4,596 | Incorporating Expressive Graphical Models in Variational Approximations:
Chain-Graphs and Hidden Variables | cs.AI | Global variational approximation methods in graphical models allow efficient
approximate inference of complex posterior distributions by using a simpler
model. The choice of the approximating model determines a tradeoff between the
complexity of the approximation procedure and the quality of the approximation.
In this ... | computer science |
4,597 | A Bayesian Multiresolution Independence Test for Continuous Variables | cs.AI | In this paper we present a method ofcomputing the posterior probability
ofconditional independence of two or morecontinuous variables from
data,examined at several resolutions. Ourapproach is motivated by
theobservation that the appearance ofcontinuous data varies widely atvarious
resolutions, producing verydifferent i... | computer science |
4,598 | Expectation Propagation for approximate Bayesian inference | cs.AI | This paper presents a new deterministic approximation technique in Bayesian
networks. This method, "Expectation Propagation", unifies two previous
techniques: assumed-density filtering, an extension of the Kalman filter, and
loopy belief propagation, an extension of belief propagation in Bayesian
networks. All three al... | computer science |
4,599 | Policy Improvement for POMDPs Using Normalized Importance Sampling | cs.AI | We present a new method for estimating the expected return of a POMDP from
experience. The method does not assume any knowledge of the POMDP and allows
the experience to be gathered from an arbitrary sequence of policies. The
return is estimated for any new policy of the POMDP. We motivate the estimator
from function-a... | computer science |
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