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

Motivation: We implement a data mining technique based on the method of Independent Component Analysis (ICA) to generate reliable independent data sets for different HIV therapies. We show that this technique takes advantage of the ICA power to eliminate the noise generated by artificial interaction of HIV system dynamics. Moreover, the incorporation of the actual laboratory data sets into the analysis phase offers a powerful advantage when compared with other mathematical procedures that consider the general behavior of HIV dynamics. Results: The ICA algorithm has been used to generate different patterns of the HIV dynamics under different therapy conditions. The Kohonen Map has been used to eliminate redundant noise in each pattern to produce a reliable data set for the simulation phase. We show that under potent antiretroviral drugs, the value of the CD4+ cells in infected persons decreases gradually by about 11% every 100 days and the levels of the CD8+ cells increase gradually by about 2% every 100 days. Availability: Executable code and data libraries are available by contacting the corresponding author. Implementation: Mathematica 4 has been used to simulate the suggested model. A Pentium III or higher platform is recommended.

Mining HIV dynamics using independent component analysis.

With rapid advances in technology and connectivity, the capability to capture data from multiple sources has given rise to multiview learning wherein each object has multiple representations and a learned model, whether supervised or unsupervised, needs to integrate these different representations. Multiview learning has shown to yield better predictive and clustering models, it also is able to provide a better insight into relationships between different views for making better decisions. In this paper, we consider the problem of multiview clustering and present a soft-hard clustering approach. In our approach, all object views are first independently mapped into a unit hypercube via soft clustering. The mapped views are next integrated via a hard clustering approach to yield the final results. Both soft and hard clustering stages utilize k-means or its variant c-means, which makes our method suitable for large-scale data problems. Furthermore, additional parallelization of the view mapping stage in parallel is possible, thus making the method more attractive for large-scale data applications. The performance of the method using three benchmark data sets is demonstrated and a comparison with other published results shows our method mostly yields a slightly better performance.

Soft-Hard Clustering for Multiview Data

Echocardiographic images are generally of poor quality. From a strict gray scale perspective, there are few edges which correspond to the underlying anatomy. Trained ultrasonographers must use considerable a priori information regarding normal human anatomy and use this information extensively to 'fill in' the missing and scanty details of a typical echocardiogram. Due to these constraints, the conventional image processing approaches often fail miserably for echocardiographic images. Knowledge-based approaches for segmenting echo images have been proposed in the recent past; these appear to be much more successful. One of the limitations of knowledge-based approaches is that the acquisition of knowledge and the formation of rules is a relatively difficult task. Moreover, there are instances where experts may have different opinions; this is specially true in the context of echocardiographic images. Additionally, it is much more difficult to codify the expert's knowledge at the image level. Considering these factors plus the typical medical research scenario, where a series of images are acquired at one sitting for a patient, we present in this paper an approach for segmenting echocardiographic image sequences by directly acquiring the expert's knowledge at the image level. The present implementation yields a look up table capturing the expert's knowledge leading to near real time segmentation of echocardiographic images. The results obtained are quite in agreement with the segmentations obtained through trained ultrasonographers.

Segmenting Echocardiographic Image Sequences Using Expert Labeling

In this paper we propose an efficient and flexible method, using the CMAC, to find the nearest neighbor of an input pattern. By searching for the nearest neighbor among a small set of probable candidates, we reduce the number of distance computations compared to the traditional approach. Unlike many other efficient techniques, this system can be trained on additional design patterns at a later time without affecting the previous learning. Experimental results are presented to demonstrate the efficiency of the proposed approach. >

Nearest neighbor classification using CMAC

Web-WISE is a system designed to address the need for efficient content-based seeking and retrieval of images on the web It supports searching by multi-features, including color and texture Web-WISE contains three automatic components, which are 1): Internet Agent responsible for searching the Web to fetch images; 2): Analysis Agent, which extracts the color and texture features of color JPEG images directly from compressed domain and 3): Query Agent, which explores the image database using certain metrics and returns a set of images (thumbnails) visually similar to the query image Due to its generic design, adding other features to the system will be very natural, including the ability to index and retrieve other types of visual information and retrieval based on edge and shape This paper presents the theoretic and practical background of the system, the schemes of these three components and the performance of the system

Ishwar K. Sethi

Papers

Mining HIV dynamics using independent component analysis.

Soft-Hard Clustering for Multiview Data

Segmenting Echocardiographic Image Sequences Using Expert Labeling

Nearest neighbor classification using CMAC

Web-WISE: Compressed Image Retrieval over the Web