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NIPS
1996
15 years 8 months ago
Radial Basis Function Networks and Complexity Regularization in Function Learning
In this paper we apply the method of complexity regularization to derive estimation bounds for nonlinear function estimation using a single hidden layer radial basis function netwo...
Adam Krzyzak, Tamás Linder
160
Voted
IJCV
2008
266views more  IJCV 2008»
15 years 6 months ago
Learning to Recognize Objects with Little Supervision
This paper shows (i) improvements over state-of-the-art local feature recognition systems, (ii) how to formulate principled models for automatic local feature selection in object c...
Peter Carbonetto, Gyuri Dorkó, Cordelia Sch...
TKDE
2008
148views more  TKDE 2008»
15 years 6 months ago
Semisupervised Clustering with Metric Learning using Relative Comparisons
Semisupervised clustering algorithms partition a given data set using limited supervision from the user. The success of these algorithms depends on the type of supervision and also...
Nimit Kumar, Krishna Kummamuru
REALWSN
2010
15 years 4 months ago
Motes in the Jungle: Lessons Learned from a Short-Term WSN Deployment in the Ecuador Cloud Forest
We study the characteristics of the communication links of a wireless sensor network in a tropical cloud forest in Ecuador, in the context of a wildlife monitoring application. Thi...
Matteo Ceriotti, Matteo Chini, Amy L. Murphy, Gian...
ICANN
2009
Springer
15 years 4 months ago
MINLIP: Efficient Learning of Transformation Models
Abstract. This paper studies a risk minimization approach to estimate a transformation model from noisy observations. It is argued that transformation models are a natural candidat...
Vanya Van Belle, Kristiaan Pelckmans, Johan A. K. ...