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.

This chapter describes some statistics of people with dyslexia. It continues with describing problems people with dyslexia experience with reading online material, and some technological aids available to help them. Three groups of university students participated in the user study of comprehension tasks using five online articles of varying complexity (as measured through Flesch-Kincaid readability grade). The study found that students with dyslexia are not slower in reading than students without dyslexia when the articles are presented in a dyslexia friendly colour scheme, but these students with dyslexia fare worse in answering correctly the questions related to the passages they read when the complexity increases.

This paper presents a survey of machine induction, studied mainly from the field of artificial intelligence, but also from the fields of pattern recognition and cognitive psychology. The paper consists of two parts: Part I discusses the basic principles and features of the machine induction process; Part II uses these principles and features to review and criticize the major supervised attribute-based induction methods. Attribute-based induction has been chosen because it is the most commonly used inductive approach in the development of expert systems and pattern recognition models.

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