Titlebook: Artificial Neural Networks - ICANN 2007; 17th International C Joaquim Marques Sá,Luís A. Alexandre,Danilo Mandic Conference proceedings 200

发出眩目光芒

空气

arthroscopy

Recurrent Bayesian Reasoning in Probabilistic Neural Networksiological neurons. We show that some parameters of PNN can be “released” for the sake of dynamic processes without destroying the statistically correct decision making. In particular, we can iteratively adapt the mixture component weights or modify the input pattern in order to facilitate the correct recognition.

变形

落叶剂

Learning Highly Non-separable Boolean Functions Using Constructive Feedforward Neural Networkn such problems with quite good results. The computational cost of training is low because most nodes and connections are fixed and only weights of one node are modified at each training step. Several examples of learning Boolean functions and results of classification tests on real-world multiclass datasets are presented.

织物

Conference proceedings 2007ICANN 2007, held in Porto, Portugal, in September 2007...The 197 revised full papers presented were carefully reviewed and selected from 376 submissions. The 98 papers of the first volume are organized in topical sections on learning theory, advances in neural network learning methods, ensemble lear

遭受

0302-9743 Networks, ICANN 2007, held in Porto, Portugal, in September 2007...The 197 revised full papers presented were carefully reviewed and selected from 376 submissions. The 98 papers of the first volume are organized in topical sections on learning theory, advances in neural network learning methods, ens

树胶

faultfinder

https://doi.org/10.1007/978-3-642-91647-2gnificance of each component in the mixture, and the other one is to discriminate the relevance of each feature to the cluster structure. The experiments on both the synthetic and real-world data show the efficacy of the proposed algorithm.

libertine

Vidya Sagar Bhasin,Indranil Mazumdaranteed. Model selection is based on predictive assessment, with efficient algorithms that allow fast greedy forward and backward selection within the class of decomposable models. We show the validity of this structure learning approach on toy data, and on two large sets of gene expression data.