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» On the Complexity of Function Learning
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JMLR
2010
119views more  JMLR 2010»
14 years 10 months ago
Semi-Supervised Learning via Generalized Maximum Entropy
Various supervised inference methods can be analyzed as convex duals of the generalized maximum entropy (MaxEnt) framework. Generalized MaxEnt aims to find a distribution that max...
Ayse Erkan, Yasemin Altun
ICASSP
2011
IEEE
14 years 7 months ago
Speaker recognition using multiple kernel learning based on conditional entropy minimization
We applied a multiple kernel learning (MKL) method based on information-theoretic optimization to speaker recognition. Most of the kernel methods applied to speaker recognition sy...
Tetsuji Ogawa, Hideitsu Hino, Nima Reyhani, Noboru...
ECAI
2004
Springer
15 years 9 months ago
Automatic Induction of Domain-Related Information: Learning Descriptors Type Domains
Abstract. Learning in complex contexts often requires pure induction to be supported by various kinds of meta-information. Providing such information is a critical, difficult and ...
Stefano Ferilli, Floriana Esposito, Teresa Maria A...
EUROPAR
2007
Springer
15 years 10 months ago
Negotiation Strategies Considering Opportunity Functions for Grid Scheduling
In Grid systems, nontrivial qualities of service have to be provided to users by the resource providers. However, resource management in a decentralized infrastructure is a complex...
Jiadao Li, Kwang Mong Sim, Ramin Yahyapour
IJCNN
2000
IEEE
15 years 8 months ago
Sensitivity Analysis for Conic Section Function Neural Networks
Sensitivity analysis is a method for extracting the cause and effect relationship between the inputs and outputs of the network. After training a neural network, one may want to k...
Lale Özyilmaz, Tülay Yildirim