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NIPS
2008
13 years 11 months ago
An interior-point stochastic approximation method and an L1-regularized delta rule
The stochastic approximation method is behind the solution to many important, actively-studied problems in machine learning. Despite its farreaching application, there is almost n...
Peter Carbonetto, Mark Schmidt, Nando de Freitas

Book
545views
15 years 6 months ago
Artificial Intelligence: A Modern Approach
"Artificial Intelligence (AI) is a big field, and this is a big book. We have tried to explore the full breadth of the field, which encompasses logic, probability, and continu...
Stuart Russell and Peter Norvig
ICML
2004
IEEE
14 years 11 months ago
Learning associative Markov networks
Markov networks are extensively used to model complex sequential, spatial, and relational interactions in fields as diverse as image processing, natural language analysis, and bio...
Benjamin Taskar, Vassil Chatalbashev, Daphne Kolle...
NCA
2007
IEEE
14 years 4 months ago
Implementing Atomic Data through Indirect Learning in Dynamic Networks
Developing middleware services for dynamic distributed systems, e.g., ad-hoc networks, is a challenging task given that such services deal with dynamically changing membership and...
Kishori M. Konwar, Peter M. Musial, Nicolas C. Nic...
AMS
2007
Springer
247views Robotics» more  AMS 2007»
14 years 4 months ago
Towards Machine Learning of Motor Skills
Autonomous robots that can adapt to novel situations has been a long standing vision of robotics, artificial intelligence, and cognitive sciences. Early approaches to this goal du...
Jan Peters, Stefan Schaal, Bernhard Schölkopf