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

This chapter presents a newly defined novel combinatorial optimisation problem, namely, the General Combinatorial Optimisation Problem (GCOP), whose decision variables are a set of elementary algorithm components. The combinations of these algorithm components, i.e. solutions of GCOP, thus represent different search algorithms. The objective of GCOP is to find the optimal combinations of algorithm components for solving optimisation problems. Solving the GCOP is thus equivalent to automatically designing the best search algorithms for optimisation problems. The definition of the GCOP is presented with a new taxonomy which categorises relevant literature on automated algorithm design into three lines of research, namely, automated algorithm configuration, selection and composition. Based on the decision space under consideration, the algorithm design itself is defined as an optimisation problem. Relevant literature is briefly reviewed, motivating a new line of exciting and challenging directions on the emerging research of automated algorithm design.

A General Model for Automated Algorithm Design

This paper formulates a vehicle routing problem where constraints have been produced from a real world forestry commissioning dataset. In the problem, vehicles are required to fully load wood from forests and then deliver the wood to sawmills. The constraints include time windows and loading bay constraints at forests and sawmills. The loading bay constraints are examples of inter-route constraints that have not been studied in the literature as much as intra-route constraints. Inter-route constraints are constraints that cause dependencies between vehicles such that more than one vehicle is required to perform a task. Some locations have a lot of consignments at similar times, causing vehicles to queue for loading bays. The aim is to produce an optimal routing of consignments for vehicles such that the total time is minimised and there is as little queuing at forests and sawmills as possible. In this paper, the problem has been formulated into a vehicle routing problem with time windows and extra inter-route constraints. An ant colony optimisation heuristic is applied to the datasets and yields feasible solutions that appropriately use the loading bays. A number of methods of handling the inter-route constraints are also tested. It is shown that incorporating the delay times at loading bays into the ant’s visibility produces solutions with the best objective values.

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Vehicle Routing in a Forestry Commissioning Operation Using Ant Colony Optimisation

The invention discloses a synthetic-aperture-radar (SAR)-image change detection method based on contourlet binary-spatial-pyramid-pooling (BSPP) networks. The method mainly solves problems of not highdetection precision and slower operation speed of SAR-image change detection in existing methods, and includes the following specific steps: (1) SAR-image preprocessing; (2) feature matrix normalization; (3) construction of a training data set; (4) construction of a contourlet BSPP region-of-interest detection network; (5) training of the region-of-interest detection network; (6) construction ofa contourlet BSPP change detection network; (7) training of the change detection network; (8) acquisition of test samples; (9) detection of regions of interest of the test samples; (10) change detection of the regions of interest; and (11) output of a change detection result graph. The method has the advantages of high detection precision and fast operation speed on SAR-image change detection.

SAR (Synthetic Aperture Radar)-image change detection method based on contourlet BSPP (binary spatial pyramid pooling) networks

The invention discloses a power transmission network topology structure design method based on particle swarm optimization. The power transmission network topology structure design method comprises the following steps: (1) setting parameters; (2) initializing swarms; (3) generating a novel individual; (4) generating novel individual optimum and global optimum; (5) generating a novel swarm; (6) judging the present iteration times, if the maximum iteration times are met, executing step (7), or else executing step (3); (7) outputting an optimal power transmission network. In the process that a power transmission network topology structure is designed, the network robustness is taken as the individual evaluation standard, effective operation such as encoding, individual operator updating and domain swarm self-generation are designed, a particle swarm optimization frame is adopted, and the power transmission network topology structure with high robustness can be designed.

Power transmission network topology structure design method based on particle swarm optimization

The invention discloses a polarimetric SAR (Synthetic Aperture Radar) terrain classification method based on superpixel and metric learning, comprising the steps of: (1) inputting images; (2) filtering; (3) segmenting pseudo color images; (4) extracting characteristics; (5) determining a sample set for selection; (6) employing a metric learning classifier to perform iteration classification, and obtaining a classification result; (7) coloring the classification result; and (8) outputting a classification result image after coloring. The polarimetric SAR terrain classification method classifies images, avoids various complex characteristic decomposition processes, has simpler and more convenient operation of characteristic extraction, keeps sound space continuity, reduces coherent speckle noise influence, and improves classification precision.

Rong Qu

Papers

A General Model for Automated Algorithm Design

Vehicle Routing in a Forestry Commissioning Operation Using Ant Colony Optimisation

SAR (Synthetic Aperture Radar)-image change detection method based on contourlet BSPP (binary spatial pyramid pooling) networks

Power transmission network topology structure design method based on particle swarm optimization

Polarimetric SAR terrain classification method based on superpixel and metric learning