Nonparametric small random networks for graph-structured pattern recognition
3 novembre 2018

Tempo di lettura: < 1 minuto Taking inspiration from the probabilistic principles underlying the topological regularities observed in random networks, the paper presents a simple and efficient Bayesian framework for…

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Enhancing Modern Supervised Word Sense Disambiguation Models by Semantic Lexical Resources
15 mai 2018

Tempo di lettura: < 1 minuto Supervised models for Word Sense Disambiguation (WSD) currently yield to state-of-the-art results in the most popular benchmarks. Despite the recent introduction…

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Dynamic Hybrid Random Fields for the Probabilistic Graphical Modeling of Sequential Data: Definitions, Algorithms, and an Application to Bioinformatics
1 octobre 2017

Tempo di lettura: < 1 minuto The paper introduces a dynamic extension of the hybrid random field (HRF), called dynamic HRF (D-HRF). The D-HRF is aimed at…

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Optimally solving permutation sorting problems with efficient partial expansion bidirectional heuristic search
30 mai 2016

Tempo di lettura: < 1 minuto In this paper we consider several variants of the problem of sorting integer permutations with a minimum number of moves, a…

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Learning as Constraint Reactions
15 août 2015

Tempo di lettura: < 1 minuto 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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Improved multi-level protein–protein interaction prediction with semantic-based regularization
15 avril 2014

Tempo di lettura: < 1 minuto 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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Experimental Guidelines for Semantic-Based Regularization
15 avril 2014

Tempo di lettura: < 1 minuto 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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Semi-supervised clustering methods
1 juillet 2013

Tempo di lettura: < 1 minuto 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 décembre 2012

Tempo di lettura: < 1 minuto 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

Tempo di lettura: < 1 minuto 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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