The invariance of an objects' identity as it transformed over time provides a powerful cue for perceptual learning. We present an unsupervised learning procedure which maximizes the mutual information between the representations adopted by a feed-forward network at consecutive time steps. We demonstrate that the network can learn, entirely unsupervised, to classify an ensemble of several patter...

Participants often categorize musical melodies (“themes”) based on perceptual features (e.g. loudness, fastness) instead of structural or relational features (e.g. pitch, rhythm) (Lamont & Dibben, 2001; Ziv & Eitan, 2007). In the present study, we investigate whether within-category analogical comparison (Markman & Gentner, 1993) influences participants to categorize musical themes based on rel...

Text classification is the process of grouping documents based on similarity in categories. Some obstacles doing text are many words appeared text, and some come up with infrequent frequency (sparse words). The way to solve this problem conduct feature selection process. There several filter-based methods; Chi-Square, Information Gain, Genetic Algorithm, Particle Swarm Optimization (PSO). Aghda...