This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, assessing predictions, handling noisy data and outliers, improving the quality of predictions by tuning t parameters, interference between old and new data, implementing locally weighted learning e ciently, and applications of locally weighted learning. A companion paper surveys how locally weighted learning can be used in robot learning and control.

In this survey, we review work in machine learning on methods for handling data sets containing large amounts of irrelevant information. We focus on two key issues: the problem of selecting relevant features, and the problem of selecting relevant examples. We describe the advances that have been made on these topics in both empirical and theoretical work in machine learning, and we present a general framework that we use to compare different methods. We close with some challenges for future work in this area.

In the past, nearest neighbor algorithms for learning from examples have worked best in domains in which all features had numeric values. In such domains, the examples can be treated as points and distance metrics can use standard definitions. In symbolic domains, a more sophisticated treatment of the feature space is required. We introduce a nearest neighbor algorithm for learning in domains with symbolic features. Our algorithm calculates distance tables that allow it to produce real-valued distances between instances, and attaches weights to the instances to further modify the structure of feature space. We show that this technique produces excellent classification accuracy on three problems that have been studied by machine learning researchers: predicting protein secondary structure, identifying DNA promoter sequences, and pronouncing English text. Direct experimental comparisons with the other learning algorithms show that our nearest neighbor algorithm is comparable or superior ...

This article describes a new system for induction of oblique decision trees. This system, OC1, combines deterministic hill-climbing with two forms of randomization to find a good oblique split (in the form of a hyperplane) at each node of a decision tree. Oblique decision tree methods are tuned especially for domains in which the attributes are numeric, although they can be adapted to symbolic or mixed symbolic/numeric attributes. We present extensive empirical studies, using both real and artificial data, that analyze OC1's ability to construct oblique trees that are smaller and more accurate than their axis-parallel counterparts. We also examine the benefits of randomization for the construction of oblique decision trees. 1. Introduction Current data collection technology provides a unique challenge and opportunity for automated machine learning techniques. The advent of major scientific projects such as the Human Genome Project, the Hubble Space Telescope, and the human brain mappi...

Model selection is important in many areas of supervised learning. Given a dataset and a set of models for predicting with that dataset, we must choose the model which is expected to best predict future data. In some situations, such as online learning for control of robots or factories, data is cheap and human expertise costly. Cross validation can then be a highly effective method for automatic model selection. Large scale cross validation search can, however, be computationally expensive. This paper introduces new algorithms to reduce the computational burden of such searches. We show how experimental design methods can achieve this, using a technique similar to a Bayesian version of Kaelbling’s Interval Estimation. Several improvements are then given, including (1) the use of blocking to quickly spot near-identical models, and (2) schemata search: a new method for quickly finding families of relevant features. Experiments are presented for robot data and noisy synthetic datasets. The new algorithms speed up computation without sacrificing reliability, and in some cases are more reliable than conventional techniques. 1

. Algorithms based on Nested Generalized Exemplar (NGE) theory (Salzberg, 1991) classify new data points by computing their distance to the nearest "generalized exemplar" (i.e., either a point or an axis-parallel rectangle). They combine the distance-based character of nearest neighbor (NN) classifiers with the axis-parallel rectangle representation employed in many rulelearning systems. An implementation of NGE was compared to the k-nearest neighbor (kNN) algorithm in 11 domains and found to be significantly inferior to kNN in 9 of them. Several modifications of NGE were studied to understand the cause of its poor performance. These show that its performance can be substantially improved by preventing NGE from creating overlapping rectangles, while still allowing complete nesting of rectangles. Performance can be further improved by modifying the distance metric to allow weights on each of the features (Salzberg, 1991). Best results were obtained in this study when the weights were co...

Current approaches to computational lexicology in language technology are knowledge-based (competence-oriented) and try to abstract away from specific formalisms, domains, and applications. This results in severe complexity, acquisition and reusability bottlenecks. As an alternative, we propose a particular performance-oriented approach to Natural Language Processing based on automatic memory-based learning of linguistic (lexical) tasks. The consequences of the approach for computational lexicology are discussed, and the application of the approach on a number of lexical acquisition and disambiguation tasks in phonology, morphology and syntax is described.

The generalization error of a function approximator, feature set or smoother can be estimated directly by the leave-one-out cross-validation error. For memory-based methods, this is computationally feasible. We describe an initial version of a general memory-based learning system (GMBL): a large collection of learners brought into a widely applicable machine-learning family. We present ongoing investigations into search algorithms which, given a dataset, find the family members and features that generalize best. We also describe GMBL's application to two noisy, difficult problems---predicting car engine emissions from pressure waves, and controlling a robot billiards player with redundant state variables. 1 Introduction The main engineering benefit of machine learning is its application to autonomous systems in which human decision making is minimized. Function approximation plays a large and successful role in this process. However, many other human decisions are needed even for si...

Given a set of models and some training data, we would like to find the model that best describes the data. Finding the model with the lowest generalization error is a computationally expensive process, especially if the number of testing points is high or if the number of models is large. Optimization techniques such as hill climbing or genetic algorithms are helpful but can end up with a model that is arbitrarily worse than the best one or cannot be used because there is no distance metric on the space of discrete models. In this paper we develop a technique called "racing" that tests the set of models in parallel, quickly discards those models that are clearly inferior and concentrates the computational effort on differentiating among the better models. Racing is especially suitable for selecting among lazy learners since training requires negligible expense, and incremental testing using leave-one-out cross validation is efficient. We use racing to select among various lazy learnin...

. Algorithms based on Nested Generalized Exemplar (NGE) theory [10] classify new data points by computing their distance to the nearest "generalized exemplar" (i.e. an axis-parallel multidimensional rectangle). An improved version of NGE, called BNGE, was previously shown to perform comparably to the Nearest Neighbor algorithm. Advantages of the NGE approach include compact representation of the training data and fast training and classification. A hybrid method that combines BNGE and the k-Nearest Neighbor algorithm, called KBNGE, is introduced for improved classification accuracy. Results from eleven domains show that KBNGE achieves generalization accuracies similar to the k-Nearest Neighbor algorithm at improved classification speed. KBNGE is a fast and easy to use inductive learning algorithm that gives very accurate predictions in a variety of domains and represents the learned knowledge in a manner that can be easily interpreted by the user. 1 Introduction Salzberg [10] describe...