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This paper presents a novel discriminative learning technique for label sequences based on a combination of the two most successful learning algorithms, Support Vector Machines and Hidden Markov Models which we call Hidden Markov Support Vector Machine. ...

We address the problem of learning distance metrics using side-information in the form of groups of "similar" points. We propose to use the RCA algorithm, which is a simple and efficient algorithm for learning a full ranked Mahalanobis metric (Shental ...

This paper is concerned with the question of how to online combine an ensemble of active learners so as to expedite the learning progress during a pool-based active learning session. We develop a powerful active learning master algorithm, based a known ...

Layout analysis is the process of extracting a hierarchical structure describing the layout of a page. In the system WISDOM++, the layout analysis is performed in two steps: firstly, the global analysis determines possible areas containing paragraphs, ...

We propose a general framework for support vector machines (SVM) based on the principle of multi-objective optimization. The learning of SVMs is formulated as a multi-objective program by setting two competing goals to minimize the empirical risk and ...

Receiver Operating Characteristic (ROC) curves provide a powerful tool for visualizing and comparing classification results. Regression Error Characteristic (REC) curves generalize ROC curves to regression. REC curves plot the error tolerance on the x-...

Designing a hypothesis test to determine the best of two machine learning algorithms with only a small data set available is not a simple task. Many popular tests suffer from low power (5×2 cv [2]), or high Type I error (Weka's 10×10 cross validation [...

In many real world applications, active selection of training examples can significantly reduce the number of labelled training examples to learn a classification function. Different strategies in the field of support vector machines have been proposed ...

We analyze the formal grounding behind Negative Correlation (NC) Learning, an ensemble learning technique developed in the evolutionary computation literature. We show that by removing an assumption made in the original work, NC can be seen to be ...

Bayesian classifiers such as Naive Bayes or Tree Augmented Naive Bayes (TAN) have shown excellent performance given their simplicity and heavy underlying independence assumptions. In this paper we introduce a classifier taking as basis the TAN model and ...

A satisfactory multiagent learning algorithm should, at a minimum, learn to play optimally against stationary opponents and converge to a Nash equilibrium in self-play. The algorithm that has come closest, WoLF-IGA, has been proven to have these two ...

We present BL-WoLF, a framework for learnability in repeated zero-sum games where the cost of learning is measured by the losses the learning agent accrues (rather than the number of rounds). The game is adversarially chosen from some family that the ...

This paper analyzes the performance of semi-supervised learning of mixture models. We show that unlabeled data can lead to an increase in classification error even in situations where additional labeled data would decrease classification error. We ...

Kernel methods have gained a great deal of popularity in the machine learning community as a method to learn indirectly in high-dimensional feature spaces. Those interested in relational learning have recently begun to cast learning from structured and ...

Support vector machines (and other kernel machines) offer robust modern machine learning methods for nonlinear classification. However, relative to other alternatives (such as linear methods, decision trees and neural networks), they can be orders of ...

Relational reinforcement learning (RRL) is a Q-learning technique which uses first order regression techniques to generalize the Q-function. Both the relational setting and the Q-learning context introduce a number of difficulties which must be dealt ...

Given a Markov decision process (MDP) with expressed prior uncertainties in the process transition probabilities, we consider the problem of computing a policy that optimizes expected total (finite-horizon) reward. Implicitly, such a policy would ...

Given a Markov chain with uncertain transition probabilities modelled in a Bayesian way, we investigate a technique for analytically approximating the mean transition frequency counts over a finite horizon. Conventional techniques for addressing this ...

The k-means algorithm is by far the most widely used method for discovering clusters in data. We show how to accelerate it dramatically, while still always computing exactly the same result as the standard algorithm. The accelerated algorithm avoids ...

We present a novel Bayesian approach to the problem of value function estimation in continuous state spaces. We define a probabilistic generative model for the value function by imposing a Gaussian prior over value functions and assuming a Gaussian ...

We consider incorporating action elimination procedures in reinforcement learning algorithms. We suggest a framework that is based on learning an upper and a lower estimates of the value function or the Q-function and eliminating actions that are not ...

We propose a method called Rule-based ESP (RESP) for utilizing prior knowledge evolving Artificial Neural Networks (ANNs). First, KBANN-likete chniques are used to transform a set of rules into an ANN, then the ANN is trained using the Enforced ...

This paper explores the problem of how to construct lazy decision tree ensembles. We present and empirically evaluate a relevance-based boosting-style algorithm that builds a lazy decision tree ensemble customized for each test instance. From the ...

We investigate how random projection can best be used for clustering high dimensional data. Random projection has been shown to have promising theoretical properties. In practice, however, we find that it results in highly unstable clustering ...

Many different metrics are used in machine learning and data mining to build and evaluate models. However, there is no general theory of machine learning metrics, that could answer questions such as: When we simultaneously want to optimise two criteria, ...

In this paper we analyze the most popular evaluation metrics for separate-and-conquer rule learning algorithms. Our results show that all commonly used heuristics, including accuracy, weighted relative accuracy, entropy, Gini index and information gain, ...

Recent theoretical results have shown that improved bounds on generalization error of classifiers can be obtained by explicitly taking the observed margin distribution of the training data into account. Currently, algorithms used in practice do not make ...

Learning algorithms from the fields of artificial neural networks and machine learning, typically, do not take any costs into account or allow only costs depending on the classes of the examples that are used for learning. As an extension of class ...

Hierarchical reinforcement learning is a general framework which attempts to accelerate policy learning in large domains. On the other hand, policy gradient reinforcement learning (PGRL) methods have received recent attention as a means to solve ...

We formulate the problem of solving stochastic linear operator equations in a Bayesian Ganssian process (GP) framework. The solution is obtained in the spirit of a collocation method based on noisy evaluations of the target function at randomly drawn or ...