Prototype learning activities

Learning Bayesian Prototype

| Given a set of samples of an unknown probability distribution, we study the problem of constructing a good approxi-mative Bayesian network model of the probability distribution in question. This task can be viewed as a search problem, where the goal is to nd a maximal probability network model, given the data. In this work, we do not make an attempt to learn arbitrarily complex multi-connecte...

Discriminative Probabilistic Prototype Learning

In this paper we propose a simple yet powerful method for learning representations in supervised learning scenarios where an input datapoint is described by a set of feature vectors and its associated output may be given by soft labels indicating, for example, class probabilities. We represent an input datapoint as a K-dimensional vector, where each component is a mixture of probabilities over ...

Prototype Abstraction in Category Learning?

Do people learn categories by abstracting prototypes, forming simple rules, remembering specific exemplars, or by some combination of these? Although some consensus seems to be emerging for a combination of rule formation and exemplar memorization, recent work has revived interest in prototype abstraction (e.g., Smith et al., 1997; Smith & Minda, 1998). We reexamined this recent evidence with a...

Adaptive prototype-based dissimilarity learning

In this thesis we focus on prototype-based learning techniques, namely three unsupervised techniques: generative topographic mapping (GTM), neural gas (NG) and affinity propagation (AP), and two supervised techniques: generalized learning vector quantization (GLVQ) and robust soft learning vector quantization (RSLVQ). We extend their abilities with respect to the following central aspects: • Ap...

Prototype for Learning Networked Ontologies