The utility of knowledge in inductive learning

In this paper, we demonstrate how different forms of background knowledge can be integrated with an inductive method for generating constant-free Horn clause rules. Furthermore, we evaluate, both theoretically and empirically, the effect that these types of knowledge have on the cost of learning a rule and on the accuracy of a learned rule. Moreover, we demonstrate that a hybrid explanation-based and inductive learning method can advantageously use an approximate domain theory, even when this theory is incorrect and incomplete

Agent-Based Distributed Learning Applied to Fraud Detection

Inductive learning and classification techniques have been applied in many problems in diverse areas. In this paper we describe an AI-based approach that combines inductive learning algorithms and meta-learning methods as a means to compute accurate classification models for detecting electronic fraud. Inductive learning algorithms are used to compute detectors of anomalous or errant behavior over inherently distributed data sets and meta-learning methods integrate their collective knowledge into higher level classification models or "meta-classifiers". By supporting the exchange of models or "classifier agents" among data sites, our approach facilitates the cooperation between financial organizations and provides unified and cross-institution protection mechanisms against fraudulent transactions. Through experiments performed on actual credit card transaction data supplied by two different financial institutions, we evaluate this approach and we demonstrate its utility

A Generalized Method for Integrating Rule-based Knowledge into Inductive Methods Through Virtual Sample Creation

Hybrid learning methods use theoretical knowledge of a domain and a set of classified examples to develop a method for classification. Methods that use domain knowledge have been shown to perform better than inductive learners. However, there is no general method to include domain knowledge into all inductive learning algorithms as all hybrid methods are highly specialized for a particular algorithm. We present an algorithm that will take domain knowledge in the form of propositional rules, generate artificial examples from the rules and also remove instances likely to be flawed. This enriched dataset then can be used by any learning algorithm. Experimental results of different scenarios are shown that demonstrate this method to be more effective than simple inductive learning

Understanding relations of individual-collective learning in work: A review of research

Abstract: A review was conducted of literature addressing learning in work, focusing on relations between individual and collective learning published in nine journals during the period 1999-2004. The journals represent three distinct fields of management/organization studies, adult education, and human resource development: all publish material about workplace learning regularly. A total of 209 articles were selected for content analysis, containing a range of material including reports of empirical research to theoretical discussion. Eight themes of individual-collective learning were identified through inductive content analysis of this literature: individual knowledge acquisition, sensemaking/reflective dialogue, levels of learning, network utility, individual human development, individuals in community, communities of practice, and a co-participation or co-emergence theme. The discussion notes apparent lack of dialogue across the fields despite similar concepts, the ontological and ideological differences among the themes of learning currently in circulation, and the low frequency of analysis of power relations in the articles reviewed

Connectionist Theory Refinement: Genetically Searching the Space of Network Topologies

An algorithm that learns from a set of examples should ideally be able to exploit the available resources of (a) abundant computing power and (b) domain-specific knowledge to improve its ability to generalize. Connectionist theory-refinement systems, which use background knowledge to select a neural network's topology and initial weights, have proven to be effective at exploiting domain-specific knowledge; however, most do not exploit available computing power. This weakness occurs because they lack the ability to refine the topology of the neural networks they produce, thereby limiting generalization, especially when given impoverished domain theories. We present the REGENT algorithm which uses (a) domain-specific knowledge to help create an initial population of knowledge-based neural networks and (b) genetic operators of crossover and mutation (specifically designed for knowledge-based networks) to continually search for better network topologies. Experiments on three real-world domains indicate that our new algorithm is able to significantly increase generalization compared to a standard connectionist theory-refinement system, as well as our previous algorithm for growing knowledge-based networks.Comment: See http://www.jair.org/ for any accompanying file

Developing theoretical rigour in inter professional education

In this chapter, the author explores the meaning of theory and the role it plays in the development of interprofessional education. The chapter explores specifically the utility of the theory of social capital in the field and uses this as a case theory to present the dimensions of theoretical quality that is proposed as essential to the advancement of research, evaluation and curriculum development in this arena