Transitioning conditional probability to discriminative classifier over inductive reasoning

Overcome context-independent probabilities based reasoning, with decomposition of categories and predicates into features as non-stable predicates. With distinction between generative classifier and discriminative classifier, we purpose to use some discriminative classifiers such as dual-form perceptron and kernelized support vector machine to improve to result of reasoning process. With capability of dual-form perceptron and kernelized support vector machine, finding linear or non-linear decision boundary for similarity-like supporting predicate for reasoning process.