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.

/pdf/statistical-pattern-recognition-a-review-1kz1b0tte4.pdf

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

It was reported that between 44,000 and 98,000 deaths occur annually as a consequence of medical errors within American hospitals alone [1] and the US National Association of Boards of Pharmacy reports that as many as 7,000 deaths occur in the US each year because of incorrect prescriptions [2] Therefore, a great desire to improve access to new healthcare methods, and the challenge of delivering healthcare becomes significant nowadays.

Multi-agent Framework Based on Web Service in Medical Data Quality Improvement for e-Healthcare Information Systems

Interacting with a multimedia information system is different from interacting with a standard text-based information system. In this paper, we present the design of a system called Content-Based Hypermedia (CBH), which allows a user to utilize metadata to intelligently browse through a collection of media objects. We describe the approach we use to model data in order to make it browsable, explore browsing (data and metadata), indicate how metadata is used in the concept of similarity, present the architecture of our system, and discuss indexing techniques for similarity browsing using content-based metadata. >

http://ieeexplore.ieee.org/iel2/3310/9942/00471675.pdf

Content-Based Hypermedia-intelligent browsing of structured media objects

If neural networks are to be used on a large scale, they have to be implemented in hardware. However, the cost of the hardware implementation is critically sensitive to factors like the precision used for the weights, the total number of bits of information and the maximum fan-in used in the network. This paper presents a version of the Constraint Based Decomposition training algorithm which is able to produce networks using limited precision integer weights and units with limited fan-in. The algorithm is tested on the 2-spiral problem and the results are compared with other existing algorithms.

/pdf/a-vlsi-optimal-constructive-algorithm-for-classification-18onm2u2bz.pdf

A VLSI optimal constructive algorithm for classification problems

The last few years have seen a remarkable growth of information in digital form due to a combination of several technological factors that include the availability of high speed electronic networks and affordable yet powerful desktop computing. This coupled with advances in scientific and consumer imaging technology-digital radiography, digital still cameras, desktop scanners, video capture boards, has created an environment where a large fraction of digital information is being created and disseminated in visual form-graphics, images, and video. This trend towards information in visual form is not surprising considering that we use vision more than any other sense to acquire and communicate information. >

Image computing for visual information systems

Closed-loop video rendering implies generating video streams in compressed form given as input video streams that are in compressed form such as JPEG or MPEG bit streams. Closed-loop video rendering has significant advantages over open-loop schemes including reduced storage requirements which in turn reduces memory disk swapping time that constitutes a large portion of the time spent in the desktop editing of massive video material. Processing in the compressed domain also offers faster processing speeds due to the smaller data size present in the compressed domain. This approach also improves image quality since multiple decompression and compression processes are avoided during the editing process. Based on MPEG, a standard video compression scheme, the authors discuss fundamental techniques, analyze pros and cons, implications and preferences for a compressed domain video rendering system. A software-based system for real-time video rendering is proposed with preliminary results demonstrating the feasibility of their closed-loop MPEG video rendering approach.

Ishwar K. Sethi

Papers

Multi-agent Framework Based on Web Service in Medical Data Quality Improvement for e-Healthcare Information Systems

Content-Based Hypermedia-intelligent browsing of structured media objects

A VLSI optimal constructive algorithm for classification problems

Image computing for visual information systems

Closed-loop MPEG video rendering