THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Data Mining Practical Machine Learning Tools and Techniques

Introduction to stochastic programming

The rapid development of real-time multimedia applications requires Quality of Service (QoS) based multicast routing in underlying computer networks. The constrained minimum Steiner tree problem in graphs as the underpinning mathematical model is a wellknown NP-complete problem. In this paper we investigate a GRASP (Greedy Randomized Adaptive Search Procedure) approach with VNS (Variable Neighborhood Search) as the local search strategy for the Delay-Constrained Least-Cost (DCLC) multicast routing problems. A large number of simulations carried out on the benchmark problems in the OR-library and a group of randomly generated graphs demonstrate that the proposed GRASP algorithm with VNS is highly efficient in solving the DCLC multicast routing problem. It outperforms other existing algorithms and heuristics in the literature.

A GRASP approach for the delay-constrained multicast routing problem

The invention discloses a rotation invariant Gabor-Zernike method based on contents, and mainly solves the problem that a required image is quickly retrieved from a great quantity of library files in medical image retrieval. The method is realized in the following processes: (1) extracting the Gabor image characteristic data of all library files; (2) extracting the Zernike image characteristic data of all library files; (3) extracting the Gabor image characteristic data of an image to be retrieved; (4) extracting the Zernike image characteristic data of the image to be retrieved; (5) carrying out similarity comparison calculation on all pieces of data; (6) increasing Gaussian noise for all images; (7) repeating the steps (1) to (5); and (8) comparing image retrieval results. The method has the outstanding advantages on an aspect of medical image retrieval, and sill can be used for quick medical image retrieval under a condition of normal noise.

Application of Gabor-Zernike characteristics in medical image retrieval

The invention discloses a polarized SAR image change detection method based on a NSCT DBN. Two to-be-detected two time phase polarized SAR images are inputted, pre-processing is carried out, diagonal elements of a coherent polarization matrix of the two polarized SAR images after pre-processing are extracted, normalization of a characteristic matrix is carried out to form a characteristic matrix based on image blocks, a training data set and a test data set are acquired, a detection model based on the NSCT DBN is constructed, the constructed data sets are utilized to train a classification model, the to-be-detected images are detected through utilizing the trained classification model. The method is advantaged in that the new NSCT DBN network model is constructed to better learn depth characteristics, change detection is finally realized, polarized SAR image change detection can be effectively realized, the result is made to be more intelligible, and detection precision is improved.

Polarized SAR image change detection method based on NSCT DBN

The invention discloses an SAR target identification method based on a depth curvelet convolution net. The SAR target identification method comprises steps of (1) acquisition of a to-be-identified image sample; (2) extraction of a curvelet characteristic image; (3) training of a depth convolution net; (5) target identification. Curvelet atoms are used as the first layer of the convolution net, and multi-dimensional characteristics are introduced in the depth convolution net, and the problems of the prior art of inability of expressing comprehensive target information caused by only adopting the single characteristic of the SAR image and slow speed caused by dependence on a lot of manual working and by adopting a shallow learning method are effectively. An image high level characteristic acquisition capability is provided by combining the image multi-dimensional characteristics and the depth convolution net, and more abundant and deeper target information is acquired, and the target identification precision of the SAR image is improved.

Rong Qu

Papers

A GRASP approach for the delay-constrained multicast routing problem

Application of Gabor-Zernike characteristics in medical image retrieval

Polarized SAR image change detection method based on NSCT DBN

SAR target identification method based on depth curvelet convolution net