Machine Learning and Its Applications, Advanced Lectures
Semantic and schematic similarities between database objects: a context-based approach
The VLDB Journal — The International Journal on Very Large Data Bases
Logic prespective on data and knowledge
Handbook of data mining and knowledge discovery
Learning from Cluster Examples
Machine Learning
A foundation for machine learning in design
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Knowledge transformers: A link between learning and creativity
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Incremental learning and concept drift in INTHELEX
Intelligent Data Analysis
Three fundamental misconceptions of Artificial Intelligence
Journal of Experimental & Theoretical Artificial Intelligence
Satisfiability of Formulas from the Standpoint of Object Classification: The RST Approach
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
Satisfiability judgement under incomplete information
Transactions on Rough Sets XI
Satisfiability of Formulas from the Standpoint of Object Classification: The RST Approach
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
Seeking Knowledge in the Deluge of Facts
Fundamenta Informaticae
A defeasible reasoning model of inductive concept learning from examples and communication
Artificial Intelligence
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In view of a great proliferation of machine learning methods and paradigms, there is a need for a general conceptual framework that would explain their interrelationships and provide a basis for their integration into multistrategy learning systems. This article presents initial results on the Inferential Theory of Learning that aims at developing such a framework, with the primary emphasis on explaining logical capabilities of learning systems, i.e., their competence. The theory views learning as a goal-oriented process of modifying the learner's knowledge by exploring the learner's experience. Such a process is described as a search through a knowledge space, conducted by applying knowledge transformation operators, called knowledge transmutations. Transmutations can be performed using any type of inference—deduction, induction, or analogy. Several fundamental pairs of transmutations are presented in a novel and very general way. These include generalization and specialization, explanation and prediction, abstraction and concretion, and similization and dissimilization. Generalization and specialization transmutations change the reference set of a description (the set of entities being described). Explanations and predictions derive additional knowledge about the reference set (explanatory or predictive). Abstractions and concretions change the level of detail in describing a reference set. Similizations and dissimilizations hypothesize knowledge about a reference set based on its similarity or dissimilarity with another reference set. The theory provides a basis for multistrategy task-adaptive learning (MTL), which is outlined and illustrated by an example. MTL dynamically adapts strategies to the learning task, defined by the input information, the learner's background knowledge, and the learning goal. It aims at synergistically integrating a wide range of inferential learning strategies, such as empirical and constructive inductive generalization, deductive generalization, abductive derivation, abstraction, similization, and others.