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» Learning for Dynamic Subsumption
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129
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IJON
2010
119views more  IJON 2010»
15 years 1 months ago
Hyperparameter learning in probabilistic prototype-based models
We present two approaches to extend Robust Soft Learning Vector Quantization (RSLVQ). This algorithm for nearest prototype classification is derived from an explicit cost functio...
Petra Schneider, Michael Biehl, Barbara Hammer
218
Voted
AR
2011
14 years 9 months ago
Learning, Generation and Recognition of Motions by Reference-Point-Dependent Probabilistic Models
This paper presents a novel method for learning object manipulation such as rotating an object or placing one object on another. In this method, motions are learned using referenc...
Komei Sugiura, Naoto Iwahashi, Hideki Kashioka, Sa...
119
Voted
AAAI
2011
14 years 2 months ago
Fast Newton-CG Method for Batch Learning of Conditional Random Fields
We propose a fast batch learning method for linearchain Conditional Random Fields (CRFs) based on Newton-CG methods. Newton-CG methods are a variant of Newton method for high-dime...
Yuta Tsuboi, Yuya Unno, Hisashi Kashima, Naoaki Ok...
ICSE
2004
IEEE-ACM
16 years 2 months ago
Finding Latent Code Errors via Machine Learning over Program Executions
This paper proposes a technique for identifying program properties that indicate errors. The technique generates machine learning models of program properties known to result from...
Yuriy Brun, Michael D. Ernst
137
Voted
ENTER
2009
Springer
15 years 9 months ago
Learning Adaptive Recommendation Strategies for Online Travel Planning
Conversational recommender systems support human-computer interaction strategies in order to assist online tourists in the important activity of dynamic packaging, i.e., in buildi...
Tariq Mahmood, Francesco Ricci, Adriano Venturini