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JMLR
2006
389views more  JMLR 2006»
14 years 11 months ago
A Very Fast Learning Method for Neural Networks Based on Sensitivity Analysis
This paper introduces a learning method for two-layer feedforward neural networks based on sensitivity analysis, which uses a linear training algorithm for each of the two layers....
Enrique Castillo, Bertha Guijarro-Berdiñas,...
GECCO
2008
Springer
121views Optimization» more  GECCO 2008»
15 years 23 days ago
Fast rule representation for continuous attributes in genetics-based machine learning
Genetic-Based Machine Learning Systems (GBML) are comparable in accuracy with other learning methods. However, efficiency is a significant drawback. This paper presents a new rep...
Jaume Bacardit, Natalio Krasnogor
KDD
2002
ACM
171views Data Mining» more  KDD 2002»
16 years 3 days ago
Mining complex models from arbitrarily large databases in constant time
In this paper we propose a scaling-up method that is applicable to essentially any induction algorithm based on discrete search. The result of applying the method to an algorithm ...
Geoff Hulten, Pedro Domingos
JMLR
2006
143views more  JMLR 2006»
14 years 11 months ago
Segmental Hidden Markov Models with Random Effects for Waveform Modeling
This paper proposes a general probabilistic framework for shape-based modeling and classification of waveform data. A segmental hidden Markov model (HMM) is used to characterize w...
Seyoung Kim, Padhraic Smyth
PAMI
2011
14 years 6 months ago
Greedy Learning of Binary Latent Trees
—Inferring latent structures from observations helps to model and possibly also understand underlying data generating processes. A rich class of latent structures are the latent ...
Stefan Harmeling, Christopher K. I. Williams