منابع مشابه
Incremental Learning from Positive Data
The present paper deals with a systematic study of incremental learning algorithms. The general scenario is as follows. Let c be any concept; then every innnite sequence of elements exhausting c is called positive presentation of c. An algorith-mic learner successively takes as input one element of a positive presentation as well as its previously made hypothesis at a time, and outputs a new hy...
متن کاملModeling Incremental Learning from Positive Data
The present paper deals with a systematic study of incremental learning algorithms. The general scenario is as follows. Let c be any concept; then every in nite sequence of elements exhausting c is called positive presentation of c. An algorithmic learner successively takes as input one element of a positive presentation as well as its previously made hypothesis at a time, and outputs a new hyp...
متن کاملIncremental Learning from Positive Examples
Classical supervised learning techniques are generally based on an inductive mechanism able to generalise a model from a set of positive examples, assuring its consistency with respect to a set of negative examples. In case of learning from positive evidence only, the problem of over-generalisation comes into account. This paper proposes a general technique for incremental multi-class learning ...
متن کاملLearning from Positive Data
Abs t rac t . Gold showed in 1967 that not even regular grammars can be exactly identified from positive examples alone. Since it is known that children learn natural grammars almost exclusively from positives examples, Gold's result has been used as a theoretical support for Chomsky's theory of innate human linguistic abilities. In this paper new results are presented which show that within a ...
متن کاملWeighing Hypotheses: Incremental Learning from Noisy Data
Incremental learning from noisy data presents dual challenges: that of evaluating multiple hypotheses incrementally and that of distinguishing errors due to noise from errors due to faulty hypotheses. This problem is critical in such areas of machine learning as concept learning, inductive programming, and sequence prediction. I develop a general, quantitative method for weighing the merits of ...
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ژورنال
عنوان ژورنال: Journal of Computer and System Sciences
سال: 1996
ISSN: 0022-0000
DOI: 10.1006/jcss.1996.0051