Concept Learning and the General-to-Specific Ordering of Hypotheses in Machine Learning

Concept Learning and the General-to-Specific Ordering of Hypotheses in Machine Learning
Author(s):
Dr Kothuri Parashu Ramulu, Dr. Jogannagary Malla Reddy
Keywords:
General-to specific, concept Learning, FIND-S, Boolean-valued Function
Abstract
The problem of inducing general functions from specific training examples is central to learning. This paper considers concept learning: acquiring the definition of a general category given a sample of positive and negative training examples of the category. Concept learning can be formulated as a problem of searching through a predefined space of potential hypotheses for the hypothesis that best fits the training examples. In many cases this search can be efficiently organized by taking advantage of a naturally occurring structure over the hypothesis space-a generalto-specific ordering of hypotheses. This chapter presents several learning algorithms and considers situations under which they converge to the correct hypothesis. We also examine the nature of inductive learning and the justification by which any program may successfully generalize beyond the observed training data
Article Details
Unique Paper ID: 156543 Publication Volume & Issue: Volume 9, Issue 4 Page(s): 134 - 137
Article Preview & Download

Share This Article

NCSST-2023

AICTE Sponsored National Conference on Smart Systems and Technologies

Last Date: 25th November 2023

LATEST INNOVATION’S AND FUTURE TRENDS IN MANAGEMENT

Last Date: 7th November 2023

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org