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

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Machine Learning : A Probabilistic Perspective

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Modern Applied Statistics With S

Low level vision in insects, its application to robot navigation

One of the fundamental issues in building autonomous agents is to be able to sense, represent and react to the world. Some of the earlier work [Mor83, Elf90, AyF89] has aimed towards a reconstructionist approach, where a number of sensors are used to obtain input that is used to construct a model of the world that mirrors the real world. Sensing and sensor fusion was thus an important aspect of such work. Such approaches have had limited success, and some of the main problems were the issues of uncertainty arising from sensor error and errors that accumulated in metric, quantitative models. Recent research has therefore looked at different ways of examining the problems. Instead of attempting to get the most accurate and correct model of the world, these approaches look at qualitative models to represent the world, which maintain relative and significant aspects of the environment rather than all aspects of the world. The relevant aspects of the world that are retained are determined by the task at hand which in turn determines how to sense. That is, task directed or purposive sensing is used to build a qualitative model of the world, which though inaccurate and incomplete is sufficient to solve the problem at hand. This paper examines the issues of building up a hierarchical knowledge representation of the environment with limited sensor input that can be actively acquired by an agent capable of interacting with the environment. Different tasks require different aspects of the environment to be abstracted out. For example, low level tasks such as navigation require aspects of the environment that are related to layout and obstacle placement. For the agent to be able to reposition itself in an environment, significant features of spatial situations and their relative placement need to be kept. For the agent to reason about objects in space, for example to determine the position of one object relative to another, the representation needs to retain information on relative locations of start and finish of the objects, that is endpoints of objects on a grid. For the agent to be able to do high level planning, the agent may need only the relative position of the starting point and destination, and not the low level details of endpoints, visual clues and so on. This indicates that a hierarchical approach would be suitable, such that each level in the hierarchy is at a different level of abstraction, and thus suitable for a different task. At the lowest level, the representation contains low level details of agent's motion and visual clues to allow the agent to navigate and reposition itself. At the next level of abstraction the aspects of the representation allow the agent to perform spatial reasoning, and finally the highest level of abstraction in the representation can be used by the agent for high level planning.

Hierarchic Representation for Spatial Knowledge

With Multimedia Information Retrieval frustrated by the seemingly intractable semantic gap, we turn to the related field of linguistics for fresh inspiration and ideas for old problems and new opportunities. An explosion in the amount and ease with which multimedia items are created and shared, courtesy of new devices and Web 2.0, prompts us to consider what happens when those items are viewed not as artefacts, or "built things", but as utterances. These conversations occur in a mixture of mediums, including text, images, audio, and video, and channels, including Twitter, Facebook, Youtube and blogs, and range in scope from one-to-one exchanges to loosely-bounded meta-conversations that cross cultures and span the globe via remixing of shared meanings like memes. We propose that MIR add to its toolbox a linguistic perspective, and highlight three useful emphases of research: genre, emergence, and effect.

Funniest thing i've seen since [href=//flic.kr/p/KGEGB]: shifting perspectives from multimedia artefacts to utterances

Determining object function based on its motion

We address policy learning with logged data in contextual bandits. Current offline-policy learning algorithms are mostly based on inverse propensity score (IPS) weighting requiring the logging policy to have \emph{full support} i.e. a non-zero probability for any context/action of the evaluation policy. However, many real-world systems do not guarantee such logging policies, especially when the action space is large and many actions have poor or missing rewards. With such \emph{support deficiency}, the offline learning fails to find optimal policies. We propose a novel approach that uses a hybrid of offline learning with online exploration. The online exploration is used to explore unsupported actions in the logged data whilst offline learning is used to exploit supported actions from the logged data avoiding unnecessary explorations. Our approach determines an optimal policy with theoretical guarantees using the minimal number of online explorations. We demonstrate our algorithms' effectiveness empirically on a diverse collection of datasets.

Svetha Venkatesh

Papers

Low level vision in insects, its application to robot navigation

Hierarchic Representation for Spatial Knowledge

Funniest thing i've seen since [href=//flic.kr/p/KGEGB]: shifting perspectives from multimedia artefacts to utterances

Determining object function based on its motion

Combining Online Learning and Offline Learning for Contextual Bandits with Deficient Support.