## A Study of Generalized Machine Learning |

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### Contents

U 6 The Use of Various Weighing Values of Training | 3 |

THE TRAINING OF ORGANIZABLE LOGICAL NETWORKS | 23 |

and Statistical States of the Trainable Logical Network | 66 |

4 other sections not shown

### Common terms and phrases

100 Retraining Trials 4-state devices assumed Axis Compressed Binocular Vision Classical Conditioning connectives considered counter cues depth perception desired distance environment equation extinction fixed goal Flow Diagram given goal circuit goal function Goal Organization ij ij ij input combination input variables interconnection kth trial learning logical function m-tuple machine Machine Learning Markov process mean number minterm Monocular notation object organization output combination output transor output variables PD(Si Primary Reinforcement punish signal response retinal disparity rewarded or punished Rote Inputs secondary reinforcement sequence sequential circuit shown in figure SOBLN solution stable stationary process stimulus Switch Configurations Three-State Statistical Switches three-state switches trainable logical network trainable network training process training technique training trial transfer Transition Matrix transition probabilities Trials Per Position unconditioned stimulus visual angle Y2 Yl Y3 Y3 Y2 Yl Yl Y3 Y2