A version space is a set of all hypotheses consistent with a given set of training examples, delimited by the specific boundary and the general boundary. In existing studies [5, 6...
A version space is a collection of concepts consistent with a given set of positive and negative examples. Mitchell [Mit82] proposed representing a version space by its boundary s...
We introduce generalized version space trees, a novel data structure that serves as a condensed representation in inductive databases for graph mining. Generalized version space tr...
Occam’s razor is the principle that, given two hypotheses consistent with the observed data, the simpler one should be preferred. Many machine learning algorithms follow this pr...
Inspired with Version Space learning, the Iterated Version Space Algorithm (IVSA) has been designed and implemented to learn disjunctive concepts. IVSA dynamically partitions its s...
The Support Vector Machine (SVM) solution corresponds to the centre of the largest sphere inscribed in version space. Alternative approaches like Bayesian Point Machines (BPM) and...
In content-based image retrieval, relevance feedback has been introduced to narrow the gap between low-level image feature and high-level semantic concept. Furthermore, to speed u...