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» On learning with dissimilarity functions
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IJCAI
1997
15 years 8 months ago
Evolvable Hardware for Generalized Neural Networks
This paper describes an evolvable hardware (EHW) system for generalized neural network learning. We have developed an ASIC VLSI chip, which is a building block to configure a scal...
Masahiro Murakawa, Shuji Yoshizawa, Isamu Kajitani...
TNN
2010
205views Management» more  TNN 2010»
15 years 1 months ago
Behavior-constrained support vector machines for fMRI data analysis
Statistical learning methods are emerging as a valuable tool for decoding information from neural imaging data. The noisy signal and the limited number of training patterns that ar...
Danmei Chen, Sheng Li, Zoe Kourtzi, Si Wu
JMLR
2012
13 years 9 months ago
A Stick-Breaking Likelihood for Categorical Data Analysis with Latent Gaussian Models
The development of accurate models and efficient algorithms for the analysis of multivariate categorical data are important and longstanding problems in machine learning and compu...
Mohammad Emtiyaz Khan, Shakir Mohamed, Benjamin M....
ECCC
2010
124views more  ECCC 2010»
15 years 6 months ago
Lower Bounds and Hardness Amplification for Learning Shallow Monotone Formulas
Much work has been done on learning various classes of "simple" monotone functions under the uniform distribution. In this paper we give the first unconditional lower bo...
Vitaly Feldman, Homin K. Lee, Rocco A. Servedio
277
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Book
778views
17 years 4 months ago
Gaussian Processes for Machine Learning
"Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning...
Carl Edward Rasmussen and Christopher K. I. Willia...