In this paper, an original method named GNG-T, extended from GNG-U algorithm by [1] is presented. The method performs continuously vector quantization over a distribution that chan...
A method for tuning MLP learning parameters in an ensemble classifier framework is presented. No validation set or cross-validation technique is required to optimize parameters for...
Abstract. We propose a two-step approach for the analysis of functional magnetic resonance images, in the context of natural stimuli. In the first step, elements of functional bra...
Jarkko Ylipaavalniemi, Eerika Savia, Ricardo Vig&a...
We study dimensionality reduction or feature selection in text document categorization problem. We focus on the first step in building text categorization systems, that is the cho...
In this paper, a new method for the determination of missing values in temporal databases is presented. This new method is based on two projection methods: a nonlinear one (Self-Or...
Antti Sorjamaa, Paul Merlin, Bertrand Maillet, Ama...
This paper presents our Recurrent Control Neural Network (RCNN), which is a model-based approach for a data-efficient modelling and control of reinforcement learning problems in di...
In a previous work, we developed a quasi-efficient maximum likelihood approach for blindly separating stationary, temporally correlated sources modeled by Markov processes. In this...
Abstract. Canonical Correlation Analysis(CCA) is a useful tool to discover relationship between different sources of information represented by vectors. The solution of the underl...
Imprecision, incompleteness, prior knowledge or improved learning speed can motivate interval–represented data. Most approaches for SVM learning of interval data use local kernel...