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Página 504
The weights connected to the output units can be adjusted in order to reduce
those errors . We can then use the error estimates of the output units to derive
error estimates for the units in the hidden layers . Finally , errors are propagated
back ...
The weights connected to the output units can be adjusted in order to reduce
those errors . We can then use the error estimates of the output units to derive
error estimates for the units in the hidden layers . Finally , errors are propagated
back ...
Página 513
Algorithm : Competitive Learning Given : A network consisting of n binary -
valued input units directly connected to any number of output units . Produce : A
set of weights such that the output units become active according to some natural
...
Algorithm : Competitive Learning Given : A network consisting of n binary -
valued input units directly connected to any number of output units . Produce : A
set of weights such that the output units become active according to some natural
...
Página 521
How can we accomplish the same task with neuronlike units ? The localist
approach is to maintain an array of sixty - four units , one unit for every possible
location ( see Figure 18 . 24 ) . A more efficient approach would be to use a group
of ...
How can we accomplish the same task with neuronlike units ? The localist
approach is to maintain an array of sixty - four units , one unit for every possible
location ( see Figure 18 . 24 ) . A more efficient approach would be to use a group
of ...
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Contenido
What Is Artificial Intelligence? | 3 |
Problems Problem Spaces and Search | 29 |
Heuristic Search Techniques | 63 |
Derechos de autor | |
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Términos y frases comunes
active addition agents algorithm allow answer apply approach assertions becomes belief build called Chapter clauses combined complete concept consider consistent constraints contains corresponding define dependency described discussed domain elements example fact Figure function given goal heuristic important initial input instance interpretation John kinds knowledge knowledge base labeled language learning logic look match meaning methods move natural necessary node object occur operators output particular path perform position possible predicate present problem procedure produce properties question reasoning relation represent representation result robot rules semantic sentence shown in Figure shows simple single situation slot solution solve space specific statements step stored structure Suppose task techniques things tree true understanding units usually variables weights