Traditional instance-based learning methods base their predictions directly on (training) data that has been stored in the memory. The predictions are based on weighting the contributions of the individual stored instances by a distance function implementing a domain-dependent similarity metrics. This basic approach suuers from three drawbacks: com-putationally expensive prediction when the dat...

This paper presents and evaluates sequential instance-based learning (SIBL), an approach to action selection based upon data gleaned from prior problem solving experiences. SIBL learns to select actions based upon sequences of consecutive states. The algorithms rely primarily on sequential observations rather than a complete domain theory. We report the results of experiments on fixed-length an...

Even though instance-based learning performs well in practice, it might be criticized for its neglect of uncertainty: An estimation is usually given in the form of a predicted label, but without characterizing the confidence of this prediction. In this paper, we propose an instancebased learning method that allows for deriving “credible” estimations, namely set-valued predictions that cover the...

The paper presents the application of the mini-models' method (MM-method) based on n-dimensional simplex for modeling of energy efficiency. The article briefly describes the problem of buildings energy performance. The mini-models method is quite new and is a subject of intensive research. The MM-method is instance-based learning algorithm. In the method, group of points which are used in the m...

A method of instance-based learning is introduced which makes use of possibility theory and fuzzy sets. Particularly, a possibilistic version of the similarity-guided extrapolation principle underlying the instancebased learning paradigm is proposed. This version is compared to the commonly used probabilistic approach from a methodological point of view. Moreover, aspects of knowledge represent...

The paper describes a new conversation management approach which may apply to a class of mixed initiative information seeking applications. It employs an application-independent conversation model inspired by the conversation theory of Grosz and Sidner [1]. Based on the model, we design a multi-layer conversation manager which employs instance-based learning (IBL) to determine conversation plan...

Metric distances and the more general concept of dissimilarities are widely used tools in instance-based learning methods and very especially in the nearestneighbor classification technique. This paper contributes to the design of general dissimilarity measures to increase their utility. The ability to understand the main properties of a hand-crafted dissimilarity measure and to alter them if n...

The principles of instance based function learning are presented. In IBFL one is given a set of positive examples of a functional predicate. These examples are true ground facts that illustrate the input output behaviour of the predicate. The purpose is then to predict the output of the predicate given a new input. Further assumptions are that there is no background theory and that the inputs a...

A relational instance-based learning algorithm, called Ribl, is motivated and developed in this paper. We argue that instancebased methods o er solutions to the often unsatisfactory behavior of current inductive logic programming (ILP) approaches in domains with continuous attribute values and in domains with noisy attributes and/or examples. Three research issues that emerge when a proposition...

A procedure founded in instance-based learning is presented, for performing a form of analogical reasoning on knowledge bases expressed in a wide range of ontology languages. The procedure exploits a novel semi-distance measure for individuals, that is based on their semantics w.r.t. a number of dimensions corresponding to a committee of features represented by concept descriptions. The procedu...