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JMLR
2012
11 years 10 months ago
Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection
We present a unifying framework for information theoretic feature selection, bringing almost two decades of research on heuristic filter criteria under a single theoretical inter...
Gavin Brown, Adam Pocock, Ming-Jie Zhao, Mikel Luj...
NECO
1998
171views more  NECO 1998»
13 years 7 months ago
Constrained Optimization for Neural Map Formation: A Unifying Framework for Weight Growth and Normalization
three different levels of abstraction: detailed models including ctivity dynamics, weight dynamics that abstract from the neural activity dynamics by an adiabatic approximation, an...
Laurenz Wiskott, Terrence J. Sejnowski
SIGMETRICS
2010
ACM
181views Hardware» more  SIGMETRICS 2010»
13 years 8 months ago
A unified modeling framework for distributed resource allocation of general fork and join processing networks
This paper addresses the problem of distributed resource allocation in general fork and join processing networks. The problem is motivated by the complicated processing requiremen...
Haiquan (Chuck) Zhao, Cathy H. Xia, Zhen Liu, Dona...
ICPR
2008
IEEE
14 years 2 months ago
Improving Bayesian Network parameter learning using constraints
This paper describes a new approach to unify constraints on parameters with training data to perform parameter estimation in Bayesian networks of known structure. The method is ge...
Cassio Polpo de Campos, Qiang Ji
JMLR
2012
11 years 10 months ago
A General Framework for Structured Sparsity via Proximal Optimization
We study a generalized framework for structured sparsity. It extends the well known methods of Lasso and Group Lasso by incorporating additional constraints on the variables as pa...
Luca Baldassarre, Jean Morales, Andreas Argyriou, ...