Inductive Supervised Quantum Learning.

Overview of Inductive Logic Programming (ILP) Systems

The current paper presents a brief overview of Inductive Logic Programming (ILP) systems. ILP algorithms are of special interest for machine learning, because most of them offer practical methods for extending the presentations used in algorithms that solve supervised learning tasks. The paper presents major approaches for solving supervised learning task, summarizes their features and classifi...

A Survey on Inductive Semi-supervised Learning

Incorporating Prior Domain Knowledge Into Inductive Supervised Machine Learning Incorporating Prior Domain Knowledge Into Inductive Machine Learning

The paper reviews the recent developments of incorporating prior domain knowledge into inductive machine learning, and proposes a guideline that incorporates prior domain knowledge in three key issues of inductive machine learning algorithms: consistency, generalization and convergence. With respect to each issue, this paper gives some approaches to improve the performance of the inductive mach...

Generalization vs. Discrimination in Learning

concept learning in animalsAnimal perceptual learningArtificial learning and Machine LearningCategorical learningClassification learningConcept formationConcept learningConcept learning of machinesLearning algorithms ReferencesFlach, P. (forthcoming). Machine Learning. The Art and Science of Algorithms that Make Sense of Data. CambridgeUniversity Press. M...

Combining ILP with Semi-supervised Learning for Web Page Categorization

This paper presents a semi-supervised learning algorithm called Iterative-Cross Training (ICT) to solve the Web pages classification problems. We apply Inductive logic programming (ILP) as a strong learner in ICT. The objective of this research is to evaluate the potential of the strong learner in order to boost the performance of the weak learner of ICT. We compare the result with the supervis...