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» Prediction on Spike Data Using Kernel Algorithms
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PAKDD
2007
ACM
224views Data Mining» more  PAKDD 2007»
15 years 3 months ago
Graph Nodes Clustering Based on the Commute-Time Kernel
This work presents a kernel method for clustering the nodes of a weighted, undirected, graph. The algorithm is based on a two-step procedure. First, the sigmoid commute-time kernel...
Luh Yen, François Fouss, Christine Decaeste...
JMLR
2008
110views more  JMLR 2008»
14 years 9 months ago
Estimating the Confidence Interval for Prediction Errors of Support Vector Machine Classifiers
Support vector machine (SVM) is one of the most popular and promising classification algorithms. After a classification rule is constructed via the SVM, it is essential to evaluat...
Bo Jiang, Xuegong Zhang, Tianxi Cai
NIPS
2007
14 years 11 months ago
Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes
We show how to use unlabeled data and a deep belief net (DBN) to learn a good covariance kernel for a Gaussian process. We first learn a deep generative model of the unlabeled da...
Ruslan Salakhutdinov, Geoffrey E. Hinton
78
Voted
ISCAS
2003
IEEE
117views Hardware» more  ISCAS 2003»
15 years 2 months ago
Learning temporal correlations in biologically-inspired aVLSI
Temporally-asymmetric Hebbian learning is a class of algorithms motivated by data from recent neurophysiology experiments. While traditional Hebbian learning rules use mean firin...
Adria Bofill-i-Petit, Alan F. Murray
BMCBI
2006
148views more  BMCBI 2006»
14 years 9 months ago
Predicting Class II MHC-Peptide binding: a kernel based approach using similarity scores
Background: Modelling the interaction between potentially antigenic peptides and Major Histocompatibility Complex (MHC) molecules is a key step in identifying potential T-cell epi...
Jesper Salomon, Darren R. Flower