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 polarimetric SAR image classification method based on a tensor MPCA, and the method can be applied to SAR image classification. The method includes 1, inputting data of three polarimetric SAR image channels; 2, converting original data into the tensor mode; 3, extracting image features; 4, training a classifier; 5, classifying the data to be classified, and acquiring the classification results. Since the data of the three polarimetric SAR image channels are converted into the tensor mode, the spatial structure information of the original data is utilized, and the classification accuracy is improved.

Polarimetric SAR image classification method based on tensor MPCA

The invention discloses a deep sparse main component analysis-based polarimetric SAR (Synthetic Aperture Radar) image classification method, and aims to mainly solve the problems of complex process and low classification accuracy of polarimetric SAR image classification in the prior art. The method is implemented by the following steps: (1) inputting a coherence matrix of a polarimetric SAR image to be classified, and filtering the coherence matrix to obtain a de-noised coherence matrix; (2) taking an element of each column vector in the de-noised coherence matrix as a sample, and randomly selecting a training sample set and a test sample set from the samples; (3) performing deep characteristic learning on the training sample set of the polarimetric SAR image, and training a two-layer sparse main component analysis network; (4) classifying the test sample set of the polarimetric SAR image to finish the classification of the polarimetric SAR image by utilizing the trained deep sparse main component analysis network. The method is high in classification accuracy, and can be used for ground object classification and target identification.

Deep sparse main component analysis-based polarimetric SAR image classification method

The literature presents many application programming interfaces (APIs) and frameworks that provide state of the art algorithms and techniques for solving optimisation problems. The same cannot be said about APIs and frameworks focused on the problem data itself because with the peculiarities and details of each variant of a problem, it is virtually impossible to provide general tools that are broad enough to be useful on a large scale. However, there are benefits of employing problem-centred APIs in a R&D environment: improving the understanding of the problem, providing fairness on the results comparison, providing efficient data structures for different solving techniques, etc. Therefore, in this work we propose a novel design methodology for an API focused on an optimisation problem. Our methodology relies on a data parser to handle the problem specification files and on a set of efficient data structures to handle the information on memory, in an intuitive fashion for researchers and efficient for the solving algorithms. Also, we present the concepts of a solution

dispenser that can manage solutions objects in memory better than built-in garbage collectors. Finally, we describe the positive results of employing a tailored API to a project involving the development of optimisation solutions for workforce scheduling and routing problems.

Towards an Efficient API for Optimisation Problems Data

In this paper, we formulate nurse scheduling problem in modern hospital environment as an integer programming (IP) problem. We simplify the nurse scheduling problem by transforming it through Information Granulation which enables the original problem to be expressed and solved more easily. The solution of the simplified problem is followed by a refinement of this solution to ensure that all constraints of the original problem are taken into account. We have used the GNU Octave, open source mathematical modelling and simulation software for Windows to solve the simplified problem expressed as Integer Programming problem. We used the branch and bound and dual simplex version of the integer programming solver. Very promising results from the real life benchmark problem sets are reported and are compared to the results reported in the literature. Results show that the proposed algorithm facilitates computation of feasible schedules in a short time. We suggest that the proposed methodology can be applied to other resource allocation problems with a large number of constraints.

New Solution for a Benchmark Nurse Scheduling Problem Using Integer Programming

The present invention discloses a hyperspectral data subspace projection and classification method based on a fuzzy label mainly for solving the problems of wrongly classified ground objects and poor data discrimination performance caused by the mixed pixels and noise in a hyperspectral image. The method comprises the steps of 1, dividing a remote sensing database sample set into a training sample and a labeled sample set; 2, calculating a discrimination term generated by the labeled sample set after the subspace projection; 3, constructing a Laplace regularization term determined by the fuzzy label of the training sample; 4, obtaining an optimal projection matrix and the fuzzy label by maximizing the difference of the discrimination term and the regularization term to realize the effective dimensionality reduction and the high-precision classification simultaneously. According to the present invention, the discrimination term is constructed by a method of discriminating the subspace projection, the data is projected to the low-dimensional space, the data discrimination performance is enhanced, and then the fuzzy label is introduced to construct the Laplace regularization, thereby solving the wrong classification problem brought by the mixed pixels, and realizing the dimensionality reduction and the high-precision classification simultaneously.

Rong Qu

Papers

Polarimetric SAR image classification method based on tensor MPCA

Deep sparse main component analysis-based polarimetric SAR image classification method

Towards an Efficient API for Optimisation Problems Data

New Solution for a Benchmark Nurse Scheduling Problem Using Integer Programming

Hyperspectral data subspace projection and classification method based on fuzzy label