A Synonym Contextual-based Process for Handling Word Similarity in Malay Sentence

In this paper, we attempt to describe a method of finding word similarity within a Malay sentence. The list of similarity word produced is based on searching the appropriate context within a Malay  sentence. The context is determined by seeking rules from a rule-based phrase database. In implementing this approach, a working prototype application is described which can be used as a tool for improving writing text in Malay language, especially well adapted toward the requirements of teaching and learning this language in primary and secondary schools. The overall concept presented in this paper will assist us to identify clearly what are the basic components and their specifications that should exist in the process. On the other hand, it is also important to point out the possible drawbacks and constraints of the practical approach suggested

The GIST of Concepts

A unified general theory of human concept learning based on the idea that humans detect invariance patterns in categorical stimuli as a necessary precursor to concept formation is proposed and tested. In GIST (generalized invariance structure theory) invariants are detected via a perturbation mechanism of dimension suppression referred to as dimensional binding. Structural information acquired by this process is stored as a compound memory trace termed an ideotype. Ideotypes inform the subsystems that are responsible for learnability judgments, rule formation, and other types of concept representations. We show that GIST is more general (e.g., it works on continuous, semi-continuous, and binary stimuli) and makes much more accurate predictions than the leading models of concept learning difficulty,such as those based on a complexity reduction principle (e.g., number of mental models,structural invariance, algebraic complexity, and minimal description length) and those based on selective attention and similarity (GCM, ALCOVE, and SUSTAIN). GIST unifies these two key aspects of concept learning and categorization. Empirical evidence from three\ud experiments corroborates the predictions made by the theory and its core model which we propose as a candidate law of human conceptual behavior

Technical note: Bias and the quantification of stability

Research on bias in machine learning algorithms has generally been concerned with the impact of bias on predictive accuracy. We believe that there are other factors that should also play a role in the evaluation of bias. One such factor is the stability of the algorithm; in other words, the repeatability of the results. If we obtain two sets of data from the same phenomenon, with the same underlying probability distribution, then we would like our learning algorithm to induce approximately the same concepts from both sets of data. This paper introduces a method for quantifying stability, based on a measure of the agreement between concepts. We also discuss the relationships among stability, predictive accuracy, and bias

Modal Similarity

Just as Boolean rules define Boolean categories, the Boolean operators define higher-order Boolean categories referred to as modal categories. We examine the similarity order between these categories and the standard category of logical identity (i.e. the modal category defined by the biconditional or equivalence operator). Our goal is 4-fold: first, to introduce a similarity measure for determining this similarity order; second, to show that such a measure is a good predictor of the similarity assessment behaviour observed in our experiment involving key modal categories; third, to argue that as far as the modal categories are concerned, configural similarity assessment may be componential or analytical in nature; and lastly, to draw attention to the intimate interplay that may exist between deductive judgments, similarity assessment and categorisation

Rules and similarity - a false dichotomy

Unless restricted to explicitly held, sharable beliefs that control and justify a person’s behavior, the notion of a rule has little value as an explanatory concept. Similarity-based processing is a general characteristic of the mind-world interface where internal processes (including explicitly represented rules) act on the external world. The distinction between rules and similarity is therefore misconceived

The sequence of conceptual information in instruction and its effect on retention

Two experiments were carried out to study the effect of the sequencing of the information in an instructional program. In both experiments, two different ordering principles were used. These principles were based on the relation between the to be learned concepts. The ordering of the information could be successive or simultaneous. The relationship between concepts is categorized either successive or coordinate. It was hypothesized that a simultaneous presentation would show better learning results than a successive presentation if between the to-be-learned concepts exists a co-ordinate relationship. A successive presentation would lead to better results in case of a successive relationship. Results suggest that the definition of both types of relationships needs refinement. Further the results show that for coordinate related concepts a simultaneous presentation is preferable

A foundation for machine learning in design

This paper presents a formalism for considering the issues of learning in design. A foundation for machine learning in design (MLinD) is defined so as to provide answers to basic questions on learning in design, such as, "What types of knowledge can be learnt?", "How does learning occur?", and "When does learning occur?". Five main elements of MLinD are presented as the input knowledge, knowledge transformers, output knowledge, goals/reasons for learning, and learning triggers. Using this foundation, published systems in MLinD were reviewed. The systematic review presents a basis for validating the presented foundation. The paper concludes that there is considerable work to be carried out in order to fully formalize the foundation of MLinD