## Contributions to perceptron theory |

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A-layer Aeronutronic analysis Appendix association units assumed assumption Binomial Distribution ceptron classification components computed CONDITIONAL PROBABILITY considered correctly identify cross-coupled denoted element elementary perceptron environment equal error correction procedure expected value f cells Figure finite number formula function G matrix hypothesis i-th A-unit input connections INPUTS PER A-UNIT likelihood ratio test linear combination logical distance Louise Hay m-vector maximum number monopolar multinomial distribution negative normally distributed number of corrections NUMBER OF EXCITATORY number of orthants number of stimuli obtained optimum origin configuration space orthants achievable output pairs positive stimulus probability measure PROBABILITY OF ERROR probability of selecting problem proof quantity random variable reinforcement rule response unit retina retinal points Rosenblatt second layer units selecting a perceptron sensory units signal solution exists stimulus is shown stimulus world subclass of perceptrons terminal distribution test stimulus threshold total number training sequence variable connections variance vector X'HX zero