boltzmann.ps

手写识别是模式识别中研究得一个热点

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612 792 0 FMBEGINPAGE
108 54 540 54 2 L
0.25 H
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0 8 Q
(Boltzmann Machines) 108 42.62 T
(December 6, 1993) 294.58 42.62 T
(4) 536 42.62 T
1 16 Q
(7.0  Conclusion) 108 709.33 T
0 12 Q
(The ideas embodied in the Boltzmann machine, and other similar networks had their ori-) 108 682 T
-0.47 (gins in neuroscience, but they are not attempts to simulate cognitive thought in any) 108 668 P
2 F
-0.47 (serious) 502.82 668 P
0 F
(way) 108 654 T
(. A better descriptive term for them might be \322associative memory\323 although they do) 127.2 654 T
(form limited) 108 640 T
2 F
(internal) 171.3 640 T
0 F
( representations of the problems that they are trying to solve. T) 209.28 640 T
(ypi-) 510.23 640 T
-0.17 (cally) 108 626 P
-0.17 (, these type of networks contain anywhere from few hundred to a few thousand units.) 130.54 626 P
(By comparison, the brain has some 10) 108 612 T
0 10 Q
(12) 291.55 616.8 T
0 12 Q
( neurons with a branching factor of about 10) 301.54 612 T
0 10 Q
(4) 514.71 616.8 T
0 12 Q
(.) 519.71 612 T
(Still, speed is) 108 598 T
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(the) 175.31 598 T
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( major hurdle to overcome, since these) 189.96 598 T
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(parallel) 378.49 598 T
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(.J. \0501985\051 \322A Learning Algorithm) 366.49 534 T
(for Boltzmann Machines.\323) 108 520 T
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(, vol 9, 147-169.) 324.84 520 T
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108 54 540 54 2 L
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(Boltzmann Machines) 108 42.62 T
(December 6, 1993) 294.58 42.62 T
(3) 536 42.62 T
3 12 Q
(\245) 108 712 T
0 F
(Let the network stabilize.) 121.75 712 T
3 F
(\245) 108 692 T
0 F
(For all neurons which have inputs and outputs active simultaneously) 121.75 692 T
(, increase the con-) 449.76 692 T
(nection strength.) 121.75 678 T
1 11 Q
(Phase II) 108 652.67 T
3 12 Q
(\245) 108 633 T
0 F
(As in phase one, clamp the binary feature vector onto the input.) 121.75 633 T
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(\245) 108 613 T
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-0.42 (Leave the outputs) 121.75 613 P
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-0.42 (, so the current internal connections will dictate what the) 269.38 613 P
(output is.) 121.75 599 T
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(\245) 108 579 T
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(Let the network stabilize.) 121.75 579 T
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(\245) 108 559 T
0 F
(For all neurons which have inputs and outputs active simultaneously) 121.75 559 T
(, decrease the con-) 449.76 559 T
(nection strength.) 121.75 545 T
1 14 Q
(5.2  Recognizing) 108 511.67 T
0 12 Q
-0.1 (This is something that Boltzmann machines do quite well. Once the network has been suf-) 108 485 P
(\336ciently trained, the input vector is simply applied to the stimulus, and the network will) 108 471 T
-0.25 (stabilize and make a decision based on the activation levels of the designated output units.) 108 457 P
(It is learning the correct associations which is dif) 108 443 T
(\336cult.) 342.95 443 T
1 16 Q
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0 12 Q
(The Boltzmann can be applied to many types of cursive handwriting recognition prob-) 108 375 T
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( stage itself. In fact, it) 429.33 217 T
(may be possible, in some circumstances, to eliminate the preprocessing stage altogether) 108 203 T
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(FIGURE 3. Detection of raw input) 257.31 106.33 T
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