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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)
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)
—ChaLearn is organizing for IJCNN 2015 an Automatic Machine Learning challenge (AutoML) to solve classification and regression problems from given feature representations, without any human intervention. This is a challenge with code submission: the code submitted can be executed automatically on the challenge servers to train and test learning machines on(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 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)
We organized a Challenge to unravel the connectivity of simulated neuronal networks. The provided data was solely based on fluorescence time series of spontaneous activity in a network constituted by 1000 neurons. The task of the participants was to compute the effective connectivity between neurons, with the goal to reconstruct as accurately as possible(More)
In machine learning, feature ranking (FR) algorithms are used to rank features by relevance to the class variable. FR algorithms are mostly investigated for the feature selection problem and less studied for the problem of ranking. This paper focuses on the latter. A question asked about the problem of ranking given in the terminology of FR is: as different(More)