Many real-world classification applications fall into the class of positive and unlabeled (PU) learning problems. In many such applications, not only could the negative training ex...
A general game player is an agent capable of taking as input a description of a game’s rules in a formal language and proceeding to play without any subsequent human input. To do...
Conventional classification learning allows a classifier to make a one shot decision in order to identify the correct label. However, in many practical applications, the problem ...
In this paper we study the identification of sparse interaction networks as a machine learning problem. Sparsity means that we are provided with a small data set and a high number...
Goele Hollanders, Geert Jan Bex, Marc Gyssens, Ron...
Abstract. Explanation based learning produces generalized explanations from examples. These explanations are typically built in a deductive manner and they aim to capture the essen...
We present Policy Gradient Actor-Critic (PGAC), a new model-free Reinforcement Learning (RL) method for creating limited-memory stochastic policies for Partially Observable Markov ...
We discuss how to learn non-recursive directed probabilistic logical models from relational data. This problem has been tackled before by upgrading the structure-search algorithm i...
Daan Fierens, Jan Ramon, Maurice Bruynooghe, Hendr...
Action-based dependency parsing, also known as deterministic dependency parsing, has often been regarded as a time efficient parsing algorithm while its parsing accuracy is a littl...
In this paper, we propose a new and general preprocessor algorithm, called CSRoulette, which converts any cost-insensitive classification algorithms into cost-sensitive ones. CSRou...
Co-training is a semi-supervised learning paradigm which trains two learners respectively from two different views and lets the learners label some unlabeled examples for each oth...