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This paper presents the use of Bernoulli mixture models for Markov blanket filtering and classification of binary data. Bernoulli mixture models can be seen as a tool for partitioning an n-dimensional hypercube, identifying regions of high data density on the corners of the hypercube. Once Bernoulli mixture models are computed from a training dataset we use(More)
Data and knowledge management systems employ feature selection algorithms for removing irrelevant, redundant, and noisy information from the data. There are two well-known approaches to feature selection, feature ranking (FR) and feature subset selection (FSS). In this paper, we propose a new FR algorithm, termed as class-dependent density-based feature(More)
ChaLearn is organizing the Automatic Machine Learning (AutoML) contest for IJCNN 2015, which challenges participants to solve classification and regression problems without any human intervention. Participants' code is automatically run on the contest servers to train and test learning machines. However, there is no obligation to submit code; half of the(More)
We are organizing a challenge to reverse engineer the structure of neuronal networks from patterns of activity recorded with calcium fluorescence imaging. Unraveling the brain structure at the neuronal level at a large scale is an important step in brain science, with many ramifications in the comprehension of animal and human intelligence and learning(More)
This paper presents the application of fuzzy set theory to automatic computer lipreading from video images. Simple rules based on fuzzy sets were generated using the mass assignment theory and were used for automatic feature extraction from video sequences. Probabilistic grid models were used to derive a knowledge base representing the visual data for(More)
In this paper we describe a model for classifying binary data using classifiers based on Bernoulli mixture models. We show how Bernoulli mixtures can be used for feature extraction and dimensionality reduction of raw input data. The extracted features are then used for training a classifier for supervised labeling of individual sample points. We have(More)
This paper describes the use of Bernoulli mixture models for extracting boolean rules from data. Bernoulli mixtures identify high data density areas on the corners of a hypercube. One corner represents a conjunction of literals in a boolean clause and the set of all identified corners , of the hypercube, indicates disjuncts of clauses to form a rule.(More)