NEWS

Learning as Constraint Reactions
15 Settembre 2015

A theory of learning is proposed,which extends naturally the classic regularization framework of kernelmachines to the case in which the agent…

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Experimental Guidelines for Semantic-Based Regularization
15 Aprile 2014

This paper presents a novel approach for learning with constraints called Semantic-Based Regularization. This paper shows how prior knowledge in form…

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Improved multi-level protein–protein interaction prediction with semantic-based regularization
15 Aprile 2014

Protein–protein interactions can be seen as a hierarchical process occurring at three related levels: proteins bind by means of specific domains, which in turn…

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Semi-supervised clustering methods
1 Luglio 2013

Cluster analysis methods seek to partition a data set into homogeneous subgroups. It is useful in a wide variety of applications,…

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Towards a Novel Probabilistic Graphical Model of Sequential Data: Fundamental Notions and a Solution to the Problem of Parameter Learning
23 Dicembre 2012

Probabilistic graphical modeling via Hybrid Random Fields (HRFs) was introduced recently, and shown to improve over Bayesian Networks (BNs) and Markov…

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Towards a Novel Probabilistic Graphical Model of Sequential Data: A Solution to the Problem of Structure Learning and an Empirical Evaluation
23 Novembre 2012

This paper develops a maximum pseudo-likelihood algorithm for learning the structure of the dynamic extension of Hybrid Random Field introduced in…

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