Passerini, Andrea - Dipartimento di Ingegneria e Scienza dell'Informazione, Universit� di Trento

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Passerini, Andrea - Dipartimento di Ingegneria e Scienza dell'Informazione, Universit� di Trento

From Margins to Probabilities in Multiclass Learning Andrea Passerini1 and Massimiliano Pontil2 and Paolo Frasconi3
Machine Learning in Structural Genomics Andrea Passerini and Alessandro Vullo
Proof Tree Kernels: a Candidate Ingredient for Intelligent
A Combination of Support Vector Machines and Bidirectional Recurrent Neural Networks for Protein
A TWO-STAGE SVM ARCHITECTURE FOR PREDICTING THE DISULFIDE BONDING
ANDREA PASSERINI Office Address
Identifying cysteines and histidines in transition metal binding sites using support vector machines and neural networks
Learning with Kernels and Logical Representations
kFOIL: Learning Simple Relational Kernels Niels Landwehr1
Kernels on Prolog Ground Terms Andrea Passerini and Paolo Frasconi
Automatic semantics extraction in law documents C.Biagioli, E.Francesconi
Mining Drug Resistance Relational Features with Hierarchical Multitask kFOIL
Learning Structured Outputs via Kernel Dependency Estimation and Stochastic
Kernels on Prolog Proof Trees: Statistical Learning in the ILP A. Passerini passerini a dsiunifiit
Predicting the Geometry of Metal Binding Sites from Protein Sequence
On the Convergence of Protein Structure and Dynamics. Statistical Learning Studies of
IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 15, NO. 1, JANUARY 2004 45 New Results on Error Correcting Output Codes of
Evaluation Methods for Focused Crawling Andrea Passerini, Paolo Frasconi, and Giovanni Soda
Predicting structural and functional sites in proteins by searching for maximum-weight cliques
Journal of Machine Learning Research xxx (2006) xxx-xxx Submitted 7/05; Revised 1/06; Published xxx/06 Kernels on Prolog Proof Trees
Improving Prediction of Zinc Binding Sites by Modeling the Linkage between Residues Close in
Learning Type Extension Trees for Metal Bonding State Paolo Frasconi1
UNIVERSIT A DEGLI STUDI DI FIRENZE
Predicting Metal-Binding Sites from Protein Sequence