Top-down Induction of Recursive Programs from Small Number of Sparse Examples

Basic representative set (BRS) is necessary for the induction of recursive concept using generalization under `-subsumption. To provide BRS, information is required about the target recursive theory which is yet to be learnt. Generalization method under inverse implication eliminates the strict necessity of the BRS, but is limited to learning very simple recursive programs. This paper proposes a new top-down approach implemented as a prototype system SMART, which learns fairly complex recursive programs from a small number of examples all lying on non-intersecting resolution path with respect to the target recursive theory. In addition, this paper illustrates some novel techniques for reducing the search complexities involved in logic program synthesis task

Constructive Induction for Recursive Programs

. This paper presents an algorithm for inducing recursive first order Horn clause programs from examples without background knowledge. This algorithm invents new predicates and their definitions exhaustively until the instances of a new predicate become the same as examples except for the name of the predicate. Our system CIRP switches into constructive induction mode using a new heuristic taking advantage of the goal directed usefulness of incomplete clauses and of the fact that it is supplied with no background knowledge. It enables CIRP to avoid exhaustive search and to overcome some difficulties associated with employing encoding length principle as a switching element for constructive induction. This paper also describes a method for deciding the argument set for a new predicate by employing the structure of the arguments of the original predicate and reports the scope, limitation and remedy of limitation of this method. 1 Introduction Due to the lack of expressive power in featu..

Logic Program Synthesis as a Controlled Search through Appropriate Hypothesis Sub-Space

This paper presents a learning system devoted to learn first order Horn clause concepts from ground concept examples and domain knowledge. Learning system employing first order representation formalism encounters an enormous, possibly infinite, hypothesis search space. We propose a new topdown ILP system, ILPCS, which, in spite of looking for the target theory in the entire hypothesis space, controls its search in the relevant hypothesis sub-space encapsulating the target concept. Efficiency and learnability resulting from our approach has been experimentally verified and compared with existing approaches

On Analysis of Multi-dimensional Features for Signature Verification

This paper aims to verify offline signatures using improved feature analysis and artificial neural network. Feature analyzer can reduce the large domain of feature space and extract invariable information. We incorporated different features from multi-dimensional feature analysis perspective. For verification from extracted features, we used neural network classifier. Instead of using feed forward neural network, multiple feed forward neural networks are used which are trained in the form of ensemble. Using such ensemble makes the system more general than a regular single neural network based system. Use of resilient back propagation for each neural network training, provides faster recognition. Using cross validation techniques, we performed significant amount of testing. Experimental evaluation of the signature verifier is reported. 1