The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.

Classification and regression trees

Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

Data Mining - Concepts and Techniques.

The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory have been receiving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.

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Statistical pattern recognition: a review

Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Planning Algorithms: Introductory Material

Multi-label classification has received more attention recently in the fields of data mining and machine learning. Though many approaches have been proposed, the critical issue of how to combine single labels to form a multi-label remains challenging. In this work, we propose a novel multi-label classification approach that each label is represented by two exclusive events: the label is selected or not selected. Then a weighted graph is used to represent all the events and their correlations. The multi-label learning is transformed into finding a constrained minimum cut of the weighted graph. In the experiments, we compare the proposed approach with the state-of-the-art multi-label classifier ML-KNN, and the results show that the new approach is efficient in terms of all the popular metrics used to evaluate multi-label classification performance.

Multi-label Classification with a Constrained Minimum Cut Model

This paper addresses the problem of building a model for text documents of interest. Specifically, it considers a scenario where a large collection of documents, for example theresult of a search on Internet using one of the popular search engines, is given. Each documentis indexed by certain keywords or terms. It is assumed that the user has identified a subset of documents that fits the user's need. The goal is to build a term association model for the documents of interest so that it can be used either for refining the user search or exported toother search engines/agents for further search of documents of interest. The model built is in the form of a unate Boolean function of the terms or keywords used in the initial search of documents. The proposed document model building algorithm is based on a modified version ofthe pocket algorithm for perceptron learning and a mapping method for converting neurons intoequivalent symbolic representations. 1. Introduction The majority of data mining applications deal with situations involving structured data, i.e. datacontaining well-defined alphanumeric fields with unambiguous meanings. This is not surprisingas most business databases contain structured data. In recent years, however, repositories ofsemi-structured or unstructured data, for example databases of text and image documents, haveshown a phenomenal growth, partly due to the popularity of Internet and decreasing hardwarecosts. The text and image data not only lack the structure of the common business data but alsoare ambiguous and generally open to several interpretations of the data. This characteristic isparticularly acute in image and video data. Consequently, management of semi or unstructured

Building a term association model for documents of interest

This paper describes a method for detecting and locating objects in images. The presented approach relies on finding general landmark candidates (glc) in an image. A glc is a closed region that can be either an object landmark (ol) or a non-object landmark (nol). The entire ol's are then grouped into object clusters (oc's). Given a set of training images, the method builds a database of ol and nol regions and oc's. When a query image is presented, we detect all of the ol's and group them into oc candidates. These cluster candidates are then classified using the oc's from the training database. This method can be used to detect and locate objects from images in many different unconstrained environments, such as detecting vehicles, speed signs, etc... The method is tested on two different cases and is shown to yield a high success rate.

Object Detection using General Landmark Regions

Neuropathic pain (NeuP) is often more difficult to treat than other types of chronic pain. The ability to predict outcomes in NeuP, such as response to specific therapies and return to work, would have tremendous value to both patients and society. In this work, we propose an adaptive clustering algorithm using the Neuropathic Pain Scale (NPS) to develop a set of standard patient templates. These templates may be useful in studying treatment response in NeuP. The approach is evaluated on 108 subjects' baseline data and results demonstrate the efficacy of utilizing neuropathic pain scale (NPS) metrics and our proposed method.

Ishwar K. Sethi

Papers

Multi-label Classification with a Constrained Minimum Cut Model

Building a term association model for documents of interest

Object Detection using General Landmark Regions

Neuropathic Pain Scale Based Clustering for Subgroup Analysis in Pain Medicine