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 invention discloses a polarization SAR image classification method based on a complex contour wave convolution neural network, and a problem of low classification accuracy in the prior art is mainly solved. The method comprises the steps of (1) inputting and normalizing a polarization coherent matrix T of a polarization SAR image to be classified, (2) according to the normalized matrix, constructing characteristic matrixes of a training data set and a test data set, (3) constructing a complex convolution neural network, and thus obtaining a complex contour wave convolution neural network, (4) training the complex contour wave convolution neural network by using the training data set, and obtaining a trained model, and (5) inputting the characteristic matrix of a test data set into the trained model to carry out classification, and obtaining a classification result. According to the method, the convolution neural network is extended to a complex domain to carry out operation and extract image characteristics of multiple scales, multiple directions and multiple resolution characteristics, the classification precision of the polarization SAR image is effectively improved, and the method can be used for target detection and identification.

Polarization SAR image classification method based on complex contour wave convolution neural network

In this paper, we compare the incorporation of Hill Climbing (HC) and Genetic Algorithm (GA) optimization in our proposed methodology in solving the examination scheduling problem. It is shown that our greedy HC optimization outperforms the GA in all cases when tested on the benchmark datasets. In our implementation, HC consumes more time to execute compared to GA which manages to improve the quality of the initial schedules in a very fast and efficient time. Despite this, since the amount of time taken by HC in producing improved schedules is considered reasonable and it never fails to produce better results, it is suggested that we incorporate the Hill Climbing optimization rather than GA in our work.

Hill Climbing versus genetic algorithm optimization in solving the examination timetabling problem

The invention discloses a neighborhood information and SVGDL (support vector guide dictionary learning)-based polarimetric SAR image classification method. The objective of the invention is to solve the problems of long operation time and low computation efficiency caused by low dictionary learning convergence rate in a polarimetric synthetic aperture radar (SAR) image classification process in the prior art. The method includes the following specific steps of: (1) inputting a polarimetric SAR images; (2) performing filtering; (3) extracting polarimetric neighborhood features; (4) performing dimensionality reduction; (5) selecting a training sample and a test sample; (6) training a dictionary and a classifier; (7) testing the dictionary and the classifier; (8) performing coloring; and (9) outputting a classification result diagram. Compared with the prior art, the neighborhood information and SVGDL (support vector guide dictionary learning)-based polarimetric SAR image classification method of the invention can effectively improve the correction rate and computation efficiency of polarimetric SAR image classification.

Neighborhood information and SVGDL (support vector guide dictionary learning)-based polarimetric SAR image classification method

The invention discloses a mean shift and neighborhood information based fuzzy C-mean image segmentation method, and mainly solves the problems of low segmentation accuracy and poor robustness of an existing image segmentation method. The method comprises the steps of (1) inputting a to-be-segmented image; (2) calculating a cluster number and an initial cluster center by adopting a mean shift algorithm; (3) performing initialization; (4) calculating a weight of an neighborhood image block in the to-be-segmented image; (5) calculating a weighted fuzzy factor weight of each pixel point in the to-be-segmented image; (6) performing cluster iteration; (7) judging whether an iterative stop condition is met or not; and (8) generating a segmented image. According to the method, the neighborhood information of the image is fully utilized, so that the method is high in noise robustness and the accuracy of image segmentation is greatly improved.

Mean shift and neighborhood information based fuzzy C-mean image segmentation method

The invention discloses a dense target feature learning-based target detection method of an optical remote-sensing image. The method mainly solves the problem that small-target information is filteredout due to deep convolution in the prior art. The method comprises the following specific steps: (1) establishing a dense target feature network with a total of 25 layers, and setting parameters of each layer; (2) constructing a training sample set and a training class label set; (3) acquiring the deep and shallow features of the dense target feature network; (4) fusing the deep and shallow features of the dense target feature network; (5) obtaining a target candidate frame feature set; (6) carrying out dense pooling; (7) constructing a test sample set; and (8) detecting the test sample set.The method has the advantages that extraction on the deep and shallow features of the optical remote-sensing image is good, and accuracy of target detection is high.

Rong Qu

Papers

Polarization SAR image classification method based on complex contour wave convolution neural network

Hill Climbing versus genetic algorithm optimization in solving the examination timetabling problem

Neighborhood information and SVGDL (support vector guide dictionary learning)-based polarimetric SAR image classification method

Mean shift and neighborhood information based fuzzy C-mean image segmentation method

Dense target feature learning-based target detection method of optical remote-sensing image