boltslides.ps

手写识别是模式识别中研究得一个热点

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(Fix up learning de\336nitions) 229.74 452 T
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(Input vector = probability) 229.74 420 T
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(Active = Active/T) 229.74 388 T
(ime) 403.65 388 T
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(Clamp desired output onto outputs.) 229.74 552 T
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(Stabilize) 229.74 526 T
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(Increase connection strength between active units) 229.74 500 T
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(Phase II) 216 464 T
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(Clamp input vector onto inputs) 229.74 436 T
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(Stabilize) 229.74 410 T
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(Decrease connection strength between active units) 229.74 384 T
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(If the sum of the active connection weights is) 229.74 612 T
(positive, then activate, otherwise deactivate.) 229.74 586 T
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(Precursor to Boltzmann Machine) 229.74 644 T
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(\245) 216 612 T
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(The Neuron) 229.74 612 T
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(eighted) 263.9 288.97 T
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(Output) 604.53 451.48 T
(\050active/inactive\051) 604.53 439.44 T
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(Motivation) 216 644 T
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(Neural networks are popular in handwriting) 229.74 574 T
(recognition systems.) 229.74 548 T
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(\245) 216 516 T
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(Should probably be given more attention.) 229.74 516 T
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(\245) 216 484 T
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(Dif) 229.74 484 T
(\336cult to formalize rules of recognition.) 261.29 484 T
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(\245) 216 452 T
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(Dif) 229.74 452 T
(\336cult to determine important features.) 261.29 452 T
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(\245) 216 420 T
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(Algorithmic analysis techniques typically have) 229.74 420 T
(many parameters to adjust.) 229.74 394 T
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