How ought we learn causal relationships? While Popper advocated a hypothetico-deductive logic of causal discovery, inductive accounts are currently in vogue. Many inductive approaches depend on the causal Markov condition as a fundamental assumption. This condition, I maintain, is not universally valid, though it is justifiable as a default assumption. In which case the results of the inductive...

How ought we learn causal relationships? While Popper advocated a hypothetico-deductive logic of causal discovery, inductive accounts are currently in vogue. Many inductive approaches depend on the causal Markov condition as a fundamental assumption. This condition, I maintain, is not universally valid, though it is justifiable as a default assumption. In which case the results of the inductive...

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...

Probabilistic planners have improved recently to the point that they can solve difficult tasks with complex and expressive models. In contrast, learners cannot tackle yet the expressive models that planners do, which forces complex models to be mostly handcrafted. We propose a new learning approach that can learn relational probabilistic models with both action effects and exogenous effects. Th...

Inductive concept learning is the task of learning to assign cases to a discrete set of classes. In real-world applications of concept learning, there are many different types of cost involved. The majority of the machine learning literature ignores all types of cost (unless accuracy is interpreted as a type of cost measure). A few papers have investigated the cost of misclassification errors. ...

Bayesian treatments of inductive inference and decision making presuppose that the structure of the situation under consideration is fully known. We are, however, often faced with having only very fragmentary information about an epistemic situation. This tension was discussed in decision theory by Savage (1954) in terms of ‘small worlds’ and ‘large worlds’ (‘grand worlds’ in Savage’s terminolo...

Here is presented a platform for automatic composition of inductive learning systems using ontologies called CAMLET, based on knowledge modeling and ontologies engineering technique. CAMLET constructs an inductive learning system with better competence to a given data set, using process and object ontologies. Afterwards, CAMLET instantiates and re nes a constructed system based on the following...

LEARNING TO RECOONIZE VISUAL CONCEPTS: DEVELOPMENf AND IMPLEMENTATION OF A METIIOD FOR TEXTURE CONCEPTS ACQUISITION TIIROUGH INDUCTIVE LEARNING JERZY WOJCIECH BALA, Ph.D. George Mason University, May 1993 Dissertation Director: Dr. Ryszard S. Michalski The goal of this research is to explore the application of symbolic learning methods to problems of computer vision. The research presented in t...

A tradeoff exists between the range of learning tasks solved by an induction system, and its performance on those tasks. We propose dynamic-bias induction, an approach in which bias is dynamically constructed as a function of the learning task. This admits the possibility of a high performance inductive learner that applies to a wide range of learning tasks. We assess the benefits and limitatio...

Much of our research has focused on the formalization of inductive inference processes and on the mathematical and philosophical foundations of inductive learning. We began rsuch work some years ago in connection with a specific problem of (formal language) learning and learnability. There we sought to develop a learning technique applicable to any member of a particular (linguistic) knowledge ...