Energy function for learning invariance in multilayer perceptron

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Energy function for learning invariance in multilayer perceptron

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A new energy function is proposed for forming self-adapting ordered representations of input samples in a multilayer perceptron. Simulation results on unconstrained handwritten digit recognition give a better invariance extraction for this model than for several other models.

Inspec keywords: character recognition; multilayer perceptrons; learning (artificial intelligence); invariance

Other keywords: learning invariance; multilayer perceptron; unconstrained handwritten digit recognition; self-adapting ordered representations; energy function; neural networks; invariance extraction

Subjects: Character recognition; Neural nets (theory)

References

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      • G. Wallis . Using spatio-temporal correlations to learn invariant object recognition. Neural Netw. , 9 , 1513 - 1519
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      • B. Olshausen , D. Field . Emergence of simple-cell receptive field properties by learning a sparsecode fornatural images. Nature , 607 - 609
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      • J. Stone , A. Bray . A learning rule for extracting spatio-temporal invariances. Netw. , 429 - 436
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      • G. Wallis , R. Baddeley . Optimal unsupervised learning in invariant object recognition. Neural Comput. , 4 , 883 - 894
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      • P. Földiák . Learning invariance from transformation sequences. Neural Comput. , 194 - 200
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