https://ai.stanford.edu/~ronnyk/wrappersPrint.pdf

Wrappers for feature subset selection

Case-based reasoning is a recent approach to problem solving and learning that has got a lot of attention over the last few years. Originating in the US, the basic idea and underlying theories have spread to other continents, and we are now within a period of highly active research in case-based reasoning in Europe, as well. This paper gives an overview of the foundational issues related to case-based reasoning, describes some of the leading methodological approaches within the field, and exemplifies the current state through pointers to some systems. Initially, a general framework is defined, to which the subsequent descriptions and discussions will refer. The framework is influenced by recent methodologies for knowledge level descriptions of intelligent systems. The methods for case retrieval, reuse, solution testing, and learning are summarized, and their actual realization is discussed in the light of a few example systems that represent different CBR approaches. We also discuss the role of case-based methods as one type of reasoning and learning method within an integrated system architecture.

/pdf/case-based-reasoning-foundational-issues-methodological-mrthek14md.pdf

Case-based reasoning: foundational issues, methodological variations, and system approaches

From the Publisher:
Pattern recognition has long been studied in relation to many different (and mainly unrelated) applications, such as remote sensing, computer vision, space research, and medical imaging. In this book Professor Ripley brings together two crucial ideas in pattern recognition; statistical methods and machine learning via neural networks. Unifying principles are brought to the fore, and the author gives an overview of the state of the subject. Many examples are included to illustrate real problems in pattern recognition and how to overcome them.This is a self-contained account, ideal both as an introduction for non-specialists readers, and also as a handbook for the more expert reader.

Pattern recognition and neural networks

Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees, classification rules, and distributed networks. However, no investigation has analyzed algorithms that use only specific instances to solve incremental learning tasks. In this paper, we describe a framework and methodology, called instance-based learning, that generates classification predictions using only specific instances. Instance-based learning algorithms do not maintain a set of abstractions derived from specific instances. This approach extends the nearest neighbor algorithm, which has large storage requirements. We describe how storage requirements can be significantly reduced with, at most, minor sacrifices in learning rate and classification accuracy. While the storage-reducing algorithm performs well on several real-world databases, its performance degrades rapidly with the level of attribute noise in training instances. Therefore, we extended it with a significance test to distinguish noisy instances. This extended algorithm's performance degrades gracefully with increasing noise levels and compares favorably with a noise-tolerant decision tree algorithm.

/pdf/instance-based-learning-algorithms-4sooiqokdz.pdf

Instance-Based Learning Algorithms

Similarity is an important and widely used concept Previous definitions of similarity are tied to a particular application or a form of knowledge representation We present an informationtheoretic definition of similarity that is applicable as long as there is a probabilistic model We demonstrate how our definition can be used to measure the similarity in a number of different domains

An Information-Theoretic Definition of Similarity

Instance-based representations have been applied to numerous classification tasks with some success Most of these applications involved predicting a symbolic class based on observed attributes This paper presents an instance-based method for predicting a numeric value based on observed attributes We prove that, given enough instances, if the numeric values are generated by continuous functions with bounded slope, then the predicted values are accurate approximations of the actual values We demonstrate the utility of this approach by comparing it with a standard approach for value prediction The instance-based approach requires neither ad hoc parameters nor background knowledge

/pdf/instance-based-prediction-of-real-valued-attributes-2sc6hlenox.pdf

Instance-based prediction of real-valued attributes

Adults can use pictorial depth cues to infer three-dimensional structure in two-dimensional depictions of objects. The age at which infants respond to the same kinds of visual information has not been determined, and theories about the underlying developmental mechanisms remain controversial. In this study, we used a visual habituation/novelty-preference procedure to assess the ability of 4-month-old infants to discriminate between two-dimensional depictions of structurally possible and impossible objects. Results indicate that young infants are sensitive to junction structures and interposition cues associated with pictorial depth and can detect inconsistent relationships among these cues that render an object impossible. Our results provide important insights into the development of mechanisms for processing pictorial depth cues that allow adults to extract three-dimensional structure from pictures of objects.

Discrimination of Possible and Impossible Objects in Infancy

It has been widely believed since Helmholtz that tangent discontinuities in image contours, such as T-junctions or L-junctions, will occur when one object occludes another. Here we describe a class of occlusion relationships where changes in 'border ownership' and amodal completion take place in the absence of tangent discontinuities in the image. We propose that even subtle curvature discontinuities can be a signal to potential changes in border ownership, and are valid ecological cues for occlusion in certain scenes.

Marc K. Albert

Papers

Instance-Based Learning Algorithms

Instance-based prediction of real-valued attributes

Discrimination of Possible and Impossible Objects in Infancy

Amodal Completion in the Absence of Image Tangent Discontinuities

Genericity in spatial vision