Nested dichotomies are a standard statistical technique for tackling certain polytomous classification problems with logistic regression. They can be represented as binary trees ...
The effort necessary to construct labeled sets of examples in a supervised learning scenario is often disregarded, though in many applications, it is a time-consuming and expensi...
We consider the problem of eliminating redundant Boolean features for a given data set, where a feature is redundant if it separates the classes less well than another feature or ...
Annalisa Appice, Michelangelo Ceci, Simon Rawles, ...
In kernel methods, an interesting recent development seeks to learn a good kernel from empirical data automatically. In this paper, by regarding the transductive learning of the k...
Surrogate maximization (or minimization) (SM) algorithms are a family of algorithms that can be regarded as a generalization of expectation-maximization (EM) algorithms. There are...
Classifier learning methods commonly assume that the training data consist of randomly drawn examples from the same distribution as the test examples about which the learned model...
This paper addresses the problem of constructing good action selection policies for agents acting in partially observable environments, a class of problems generally known as Part...
A standard method for approximating averages in probabilistic models is to construct a Markov chain in the product space of the random variables with the desired equilibrium distr...
We introduce a framework, which we call Divide-by-2 (DB2), for extending support vector machines (SVM) to multi-class problems. DB2 offers an alternative to the standard one-again...