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

Full truckload transportation (FTL) in the form of freight containers represents one of the most important transportation modes in international trade. Due to large volume and scale, in FTL, delivery time is often less critical but cost and service quality are crucial. Therefore, efficiently solving large scale multiple shift FTL problems is becoming more and more important and requires further research. In one of our earlier studies, a set covering model and a three-stage solution method were developed for a multi-shift FTL problem. This paper extends the previous work and presents a significantly more efficient approach by hybridising pricing and cutting strategies with metaheuristics (a variable neighbourhood search and a genetic algorithm). The metaheuristics were adopted to find promising columns (vehicle routes) guided by pricing and cuts are dynamically generated to eliminate infeasible flow assignments caused by incompatible commodities. Computational experiments on real-life and artificial benchmark FTL problems showed superior performance both in terms of computational time and solution quality, when compared with previous MIP based three-stage methods and two existing metaheuristics. The proposed cutting and heuristic pricing approach can efficiently solve large scale real-life FTL problems.

A Hybrid Pricing and Cutting Approach for the Multi-Shift Full Truckload Vehicle Routing Problem

The invention discloses a high resolution SAR image classification method based on the deep convolutional step network To-be-classified high resolution SAR images and the mark information thereof are inputted; a training data set D1 and a test data set D2 are constructed; normalization of characteristics of the data sets D1 and D2 is respectively carried out to acquire data sets D3 and D4; a classifier model based on the deep convolutional step network is constructed; the network is trained through utilizing the training data set D3; the test data set D4 is classified through utilizing the trained classification model The method is advantaged in that a few of labeled training samples can be fully utilized, the convolutional layer is further employed to effectively extract high-layer discrimination characteristics, and thereby relatively high classification precision can be realized

High resolution SAR image classification method based on deep convolutional step network

The invention discloses a polarized SAR image classification method based on a multi-scale depth directional wavelet network. The method comprises the steps that (1) a polarized SAR image is input; (2) Pauli decomposition characteristics are extracted; (3) a characteristic matrix of a training sample is constructed; (4) the multi-scale depth directional wavelet network is initialized; (5) the multi-scale depth directional wavelet network is trained; (6) a characteristic matrix of a test sample is constructed; (7) a classification standard of the test sample is obtained; (8) the classification precision is calculated; (9) coloring is carried out; and (10) the colored polarized SAR image is output. According to the invention, directional wavelet filters of different scales serve as filters of the multi-scale depth directional wavelet network, the polarized SAR image is classified, and the method has the advantages that directional and global characteristics of the polarized SAR image are reserved effectively.

Polarized SAR image classification method based on multi-scale depth directional wavelet network

The present invention discloses a hyperspectral image classification method based on a deep integrated residual network. Defects are overcome that human selection of weak classifiers and design of anintegration method are complex and time-consuming, the computation process is tedious and a classification result has phenomena of the same object with different spectra characteristics and differentobjects with the same spectra characteristic caused by a semi-supervised training mode in the prior art. The implementation of the method comprises the steps of: (1) inputting multispectral images; (2) performing ground object target removal normalization processing of images of each wave band of each multispectral image; (3) obtaining a multispectral image matrix; (4) obtaining a data set; (5) establishing a deep integrated residual network; (6) training the deep integrated residual network; and (7) performing classification of the test data set. The multispectral image classification methodbased on the deep integrated residual network has complete multispectral image features for learning, is more concise and much clearer in process, allows a classification effect to be more accurate, and can be used for classification of hyperspectral images.

Multispectral image classification method based on deep integrated residual network

The invention discloses a Pauli decomposition and depth residual network-based polarimetric SAR image classification method. The method mainly solves the problems in the prior art that the classification accuracy is low and the neural network cannot be increased more deeply. According to the technical scheme of the invention, the method comprises the steps of inputting a to-be-classified polarimetric SAR image, subjecting a polarization scattering matrix S to Pauli decomposition, and forming a pixel-based feature matrix F; representing an original element value by 28*28 blocks around each element in the feature matrix F and forming an image block-based feature matrix; constructing a training data set D; subjecting the image to super-pixel treatment after the Pauli decomposition treatment and forming a data set T1; constructing a classification model based on a depth residual network; training the classification model by using the training data set so as to obtain a well trained model; inputting the data set T1 into the well trained model to classify the data set T1 and then obtaining a predictive label matrix T2 of the entire image; removing the pixels of the training data set from the matrix T2 and then calculating the accuracy. The method of the invention adopts the depth residual network, so that network layers are increased. Meanwhile, the image is processed by adopting super pixels, so that the features of the image are effectively learnt. The classification accuracy of polarimetric SAR images is improved. The method can be used for target recognition.

Rong Qu

Papers

A Hybrid Pricing and Cutting Approach for the Multi-Shift Full Truckload Vehicle Routing Problem

High resolution SAR image classification method based on deep convolutional step network

Polarized SAR image classification method based on multi-scale depth directional wavelet network

Multispectral image classification method based on deep integrated residual network

Pauli decomposition and depth residual network-based polarimetric SAR image classification method