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NIPS
2008
15 years 8 months ago
Learning with Consistency between Inductive Functions and Kernels
Regularized Least Squares (RLS) algorithms have the ability to avoid over-fitting problems and to express solutions as kernel expansions. However, we observe that the current RLS ...
Haixuan Yang, Irwin King, Michael R. Lyu
JMLR
2002
133views more  JMLR 2002»
15 years 6 months ago
Learning Precise Timing with LSTM Recurrent Networks
The temporal distance between events conveys information essential for numerous sequential tasks such as motor control and rhythm detection. While Hidden Markov Models tend to ign...
Felix A. Gers, Nicol N. Schraudolph, Jürgen S...
WINE
2005
Springer
136views Economy» more  WINE 2005»
16 years 6 days ago
Click Fraud Resistant Methods for Learning Click-Through Rates
Abstract. In pay-per-click online advertising systems like Google, Overture, or MSN, advertisers are charged for their ads only when a user clicks on the ad. While these systems ha...
Nicole Immorlica, Kamal Jain, Mohammad Mahdian, Ku...
TNN
1998
146views more  TNN 1998»
15 years 6 months ago
Fuzzy lattice neural network (FLNN): a hybrid model for learning
— This paper proposes two hierarchical schemes for learning, one for clustering and the other for classification problems. Both schemes can be implemented on a fuzzy lattice neu...
Vassilios Petridis, Vassilis G. Kaburlasos
ICDAR
2009
IEEE
16 years 1 months ago
Learning and Adaptation for Improving Handwritten Character Recognizers
Writer independent handwriting recognition systems are limited in their accuracy, primarily due the large variations in writing styles of most characters. Samples from a single ch...
Naveen Chandra Tewari, Anoop M. Namboodiri