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Multiplying both sides of this result by wT and adding w0, and making use of y(x)=wTx+w0 and y(xΓ)=wTxΓ+w0=0, we have r=y(x)/||w||. The idea proposed 阅读全文
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Basically, the support vector machine is a binary learning machine with some highly elegant properties. Given a training sample, the support vector ma 阅读全文
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When neuron j is located in a hidden layer of the network, there is no specified desired response for that neuron. For this derivative to exist, we re 阅读全文
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When neuron j is located in the output layer of the network, it is supplied with a desired response of its own. If neuron j is in the first hidden lay 阅读全文
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An elegant and powerful method for finding maximum likelihood solutions for models with latent variables is called the expectation-maximization algori 阅读全文