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

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. In order to present a unified treatment of machine learning problems and solutions, it discusses many methods from different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The text covers such topics as supervised learning, Bayesian decision theory, parametric methods, multivariate methods, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, and reinforcement learning. New to the second edition are chapters on kernel machines, graphical models, and Bayesian estimation; expanded coverage of statistical tests in a chapter on design and analysis of machine learning experiments; case studies available on the Web (with downloadable results for instructors); and many additional exercises. All chapters have been revised and updated. Introduction to Machine Learning can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods. Adaptive Computation and Machine Learning series

Introduction to Machine Learning

A major new professional reference work on fingerprint security systems and technology from leading international researchers in the field Handbook provides authoritative and comprehensive coverage of all major topics, concepts, and methods for fingerprint security systems This unique reference work is an absolutely essential resource for all biometric security professionals, researchers, and systems administrators

/pdf/handbook-of-fingerprint-recognition-16mh887j24.pdf

Handbook of Fingerprint Recognition

In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

https://isiarticles.com/bundles/Article/pre/pdf/24700.pdf

Applying Logistic Regression to the Fusion of the NIST'99 1-Speaker Submissions

Multi-modal identity verification using expert fusion

The contribution of this paper is twofold: (1) to formulate a decision fusion problem that is encountered in the design of a multi-modal identity verification system as a particular classification problem, and (2) to solve this problem by using a support vector machine (SVM). The multi-modal identity verification system under consideration is built of d modalities in parallel, each one delivering as output a scalar number, called a score, stating how well the claimed identity is verified. A fusion module receiving the d scores as input has to take a binary decision: to accept or reject the identity. This fusion problem has been solved using SVMs. The performance of this fusion module has been evaluated and compared with other proposed methods on a multi-modal database containing both vocal and visual modalities.

Multi-modal identity verification using support vector machines (SVM)

An approach is presented to the long range automatic detec- tion of vehicles, using multisensor image sequences. The method is tested on a database of multispectral image sequences, acquired under diverse operational conditions. The approach consists of two parts. The first part uses a semisupervised approach, based on texture parameters, for detecting stationary targets. For each type of sensor one learning image is chosen. Texture parameters are calculated at each pixel of the learning images and are combined using logistic regression into a value that represents the conditional probability that the pixel belongs to a target given the texture parameters. The actual detection algorithm ap- plies the same combination to the texture features calculated on the remainder of the database (test images). When the results of this feature-level fusion are stored as an image, the local maxima correspond to likely target positions. These feature-level-fused images are calcu- lated for each sensor. In a sensor fusion step, the results obtained per sensor are then combined again. Region growing around the local maxima is then used to detect the targets. The second part of the algo- rithm searches for moving targets. To detect moving vehicles, any mo- tion of the sensor must be detected first. If sensor motion is detected, it is estimated using a Markov random field model. Available prior knowl- edge about the sensor motion is used to simplify the motion estimation. The estimate is used to warp past images onto the current image in a temporal fusion approach and moving targets are detected by threshold- ing the difference between the original and warped images. Decision level fusion combines the results from both parts of the algorithm.

/pdf/multilevel-data-fusion-for-the-detection-of-targets-using-53m5naomxe.pdf

Multilevel data fusion for the detection of targets using multispectral image sequences

The aim of this paper is to propose a strategy that uses data fusion at three different levels to gradually improve the performance of an identity verification system. In a first step temporal data fusion can be used to combine multiple instances of a single (mono-modal) expert to reduce its measurement variance. If system performance after this first step is not good enough to satisfy the end-user's needs, one can improve it by fusing in a second step result of multiple experts working on the same modality. For this approach to work, it is supposed that the respective classification errors of the different experts are de-correlated. Finally, if the verification system's performance after this second step is still not good enough, one will be forced to move onto the third step in which performance can be improved by using multiple experts working on different (biometric) modalities. To be useful however, these experts have to be chosen in such a way that adding the extra modalities increases the separation in the multi-dimensional modality-space between the distributions of the different populations that have to be classified by the system. This kind of level-based strategy allow to gradually tune the performance of an identity verification system to the end-user's requirements while controlling the increase of investment costs. In this paper results of several fusion modules will be shown at each level. All experiments have been performed on the same multi-modal database to be able to compare the gain in performance each time one goes up a level.

Patrick Verlinde

Papers

Applying Logistic Regression to the Fusion of the NIST'99 1-Speaker Submissions

Multi-modal identity verification using expert fusion

Multi-modal identity verification using support vector machines (SVM)

Multilevel data fusion for the detection of targets using multispectral image sequences

Multilevel data fusion approach for gradually upgrading the performances of identity verification systems