## Incremental concept learning for bounded data mining (1999)

### Cached

### Download Links

Venue: | Information and Computation |

Citations: | 39 - 29 self |

### BibTeX

@ARTICLE{Jain99incrementalconcept,

author = {Sanjay Jain and Steffen Lange and Thomas Zeugmann},

title = {Incremental concept learning for bounded data mining},

journal = {Information and Computation},

year = {1999}

}

### Years of Citing Articles

### OpenURL

### Abstract

Important re nements of concept learning in the limit from positive data considerably restricting the accessibility of input data are studied. Let c be any concept; every in nite sequence of elements exhausting c is called positive presentation of c. In all learning models considered the learning machine computes a sequence of hypotheses about the target concept from a positive presentation of it. With iterative learning, the learning machine, in making a conjecture, has access to its previous conjecture and the latest data item coming in. In k-bounded example-memory inference (k is a priori xed) the learner is allowed to access, in making a conjecture, its previous hypothesis, its memory of up to k data items it has already seen, and the next element coming in. In the case of k-feedback identi cation, the learning machine, in making a conjecture, has access to its previous conjecture, the latest data item coming in, and, on the basis of this information, it can compute k items and query the database of previous data to nd out, for each of the k items, whether or not it is in the database (k is again a priori xed). In all cases, the sequence of conjectures has to converge to a hypothesis