Ensemble methods are learning algorithms that construct a set of classi ers and then classify new data points by taking a weighted vote of their predictions The original ensemble method is Bayesian… (More)

This paper presents a new approach to hierarchical reinforcement learning based on decomposing the target Markov decision process (MDP) into a hierarchy of smaller MDPs and decomposing the value… (More)

This article reviews five approximate statistical tests for determining whether one learning algorithm outperforms another on a particular learning task. These tests are compared experimentally to… (More)

Bagging and boosting are methods that generate a diverse ensemble of classifiers by manipulating the training data given to a “base” learning algorithm. Breiman has pointed out that they rely for… (More)

Multiclass learning problems involve nding a de nition for an unknown function f(x) whose range is a discrete set containing k > 2 values (i.e., k \classes"). The de nition is acquired by studying… (More)

Machine Learning research has been making great progress in many directions This article summarizes four of these directions and discusses some current open problems The four directions are a… (More)

In many domains, an appropriate inductive bias is the MIN-FEATURES bias, which prefers consistent hypotheses deenable over as few features as possible. This paper deenes and studies this bias. First,… (More)

The multiple instance problem arises in tasks where the training examples are ambiguous: a single example object may have many alternative feature vectors (instances) that describe it, and yet only… (More)

This paper presents a new approach to hierarchical reinforcement learning based on the MAXQ decomposition of the value function. The MAXQ decomposition has both a procedural semantics—as a subroutine… (More)