Two experiments are reported in which four dependent variables; typicality ratings, response times, category-naming frequencies, and exemplar-generation frequencies of natural language concepts, were predicted by two sorts of prototype predictors and by an exemplar predictor related to Heit and Barsalou’s (1996) instantiation principle. In the first experiment, the exemplar predictor was compar...

In this paper, we propose a new approach for domain generalization by exploiting the low-rank structure from multiple latent source domains. Motivated by the recent work on exemplar-SVMs, we aim to train a set of exemplar classifiers with each classifier learnt by using only one positive training sample and all negative training samples. While positive samples may come from multiple latent doma...

Making accurate judgments is an essential skill in everyday life. Although how different memory abilities relate to categorization and judgment processes has been hotly debated, the question is far from resolved. We contribute to the solution by investigating how individual differences in memory abilities affect judgment performance in 2 tasks that induced rule-based or exemplar-based judgment ...

In this paper, it is shown that the Feature-Extracting Bidirectional Associative Memory (FEBAM) can encompass competitive model features based on winner-take-all, kwinners-take-all and self-organizing feature map properties. The modified model achieves perceptual multidimensional feature extraction, cluster-based category formation through simultaneous creation of prototype/exemplar memories, a...

Recognizing and reasoning about the objects found in an image is one of the key problems in computer vision. This thesis is based on the idea that in order to understand a novel object, it is often not enough to recognize the object category it belongs to (i.e., answering “What is this?”). We argue that a more meaningful interpretation can be obtained by linking the input object with a similar ...

This paper describes a novel exemplar-based spectral conversion (SC) system developed by the AST (Academia Sinica, Taipei) team for the 2016 voice conversion challenge (vcc2016). The key feature of our system is that it integrates the locally linear embedding (LLE) algorithm, a manifold learning algorithm that has been successfully applied for the super-resolution task in image processing, with...

Four experiments are presented that competitively test rule- and exemplar-based models of human categorization behavior. Participants classified stimuli that varied on a unidimensional axis into 2 categories. The stimuli did not consistently belong to a category; instead, they were probabilistically assigned. By manipulating these assignment probabilities, it was possible to produce stimuli for...