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

Taking inspiration from the probabilistic principles underlying the topological regularities observed in random networks, the paper presents a simple and efficient Bayesian framework for…

Read More
Enhancing Modern Supervised Word Sense Disambiguation Models by Semantic Lexical Resources
15 Maggio 2018

Supervised models for Word Sense Disambiguation (WSD) currently yield to state-of-the-art results in the most popular benchmarks. Despite the recent introduction…

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

The paper introduces a dynamic extension of the hybrid random field (HRF), called dynamic HRF (D-HRF). The D-HRF is aimed at…

Read More
Optimally solving permutation sorting problems with efficient partial expansion bidirectional heuristic search
30 Maggio 2016

In this paper we consider several variants of the problem of sorting integer permutations with a minimum number of moves, a…

Read More
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…

Read More
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…

Read More
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…

Read More
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,…

Read More
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…

Read More
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…

Read More