A review of machine learning

Abstract

This paper reviews Machine Learning (ML), and extends and complements previous work (Kocabas, 1991; Kalkanis and Conroy, 1991). Although this paper focuses on inductive learning, it at least touches on a great many aspects of ML in general. In addition, incremental induction is also reviewed. Therefore, a general review of ML is presented, but specific detail which has been covered previously is omitted, although other relevant references are noted, and later material is commented upon.

Analysis of Parametric & Non Parametric Classifiers for Classification Technique using WEKA

“…A Large number of classification techniques were developed by the means of statistical method, logical method , symbolic method, perceptron based method, fuzzy based concept, Neural network based and rule based etc. D. Dutton & G. Conroy et al [10] provided brief review of what Machine Learning algorithims has done. De Mantaras and Armengol also reported historical survey of logic and instance based learning classifiers [11].…”

Machine learning: a review of classification and combining techniques

“…We have limited our references to recent refereed journals, published books and conferences. A brief review of what ML includes can be found in (Dutton and Conroy 1996). De Mantaras and Armengol (1998) also presented a historical survey of logic and instance based learning classifiers.…”

Personalised hypermedia presentation techniques for improving online customer relationships

“…Several machine learning algorithms (Dutton & Conroy, 1997) have been applied to interface agents. Memory-based learning (a kind of case-based learning), reinforcement learning and induction of decision trees (ID3) are used in scheduling agents (Kozierok & Maes, 1993;Mitchell et al, 1994).…”