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

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Principles Of Optics Electromagnetic Theory Of Propagation Interference And Diffraction Of Light

This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Statistical Pattern Recognition

This paper combines histarical lineage, assessment of the state of the art, and discussion of unsolved problems in robot force control. The difference between continuous and logic branching strateg...

Historical perspective and state of the art in robot force control

The detection and recognition of objects in images is a key research topic in the computer vision community Within this area, face recognition and interpretation has attracted increasing attention owing to the possibility of unveiling human perception mechanisms, and for the development of practical biometric systems This book and the accompanying website, focus on template matching, a subset of object recognition techniques of wide applicability, which has proved to be particularly effective for face recognition applications Using examples from face processing tasks throughout the book to illustrate more general object recognition approaches, Roberto Brunelli: examines the basics of digital image formation, highlighting points critical to the task of template matching; presents basic and advanced template matching techniques, targeting grey-level images, shapes and point sets; discusses recent pattern classification paradigms from a template matching perspective; illustrates the development of a real face recognition system; explores the use of advanced computer graphics techniques in the development of computer vision algorithms Template Matching Techniques in Computer Vision is primarily aimed at practitioners working on the development of systems for effective object recognition such as biometrics, robot navigation, multimedia retrieval and landmark detection It is also of interest to graduate students undertaking studies in these areas

Template Matching Techniques in Computer Vision: Theory and Practice

This paper examines some issues concerning the inverse kinematics and statics of cable-suspended robots and studies some of the inherent workspace limitations that result from the fact that the robot is cable actuated. The paper presents necessary and sufficient conditions for a cable-suspended robot to stay in a given configuration (i.e., to achieve static equilibrium). Another important issue is the extent to which the cables constrain the robot. For example, fully constraining the robot is critical for space applications in which the robot must work in a zero-gravity environment. Conditions for completely constraining the robot are derived. The problems of achieving static equilibrium and fully constraining the robot are formulated in terms of the left null space of a manipulator inverse Jacobian. This null space formulation is also used to study the fault tolerance of cable-suspended robots that are redundantly actuated. © 1998 John Wiley & Sons, Inc.

On the inverse kinematics, statics, and fault tolerance of cable‐suspended robots

When a manipulator suffers a joint failure, its performance can be significantly affected. If the failed joint is locked, the resulting manipulator Jacobian is given by the original Jacobian, except that the column associated with the failed joint is removed. The rank of the resulting Jacobian then determines if the manipulator still has the ability to perform arbitrary end-effector motions. Unfortunately, even at an operating configuration that has a relatively high manipulability index, a joint failure may still result in a singular Jacobian. This work examines the problem of determining the reduced manipulability of a manipulator after one or more joint failures. Configurations that result in a minimal reduction of the manipulability index for any set of joint failures are determined.

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A local measure of fault tolerance for kinematically redundant manipulators

This paper analyzes the effects of the mechanical stiffness of end effector force sensors on the control of robot manipulators. Modeling of the general manipulator-sensor system is presented, along with a detailed investigation of a single joint case. Analytical and experimental results indicate that the use of a mechanically compliant sensor permits a more responsive force controlled system to be realized. In addition, an active compensator for sensor deflections was developed to recover the static positional stiffness of the manipulator.

The effect of wrist force sensor stiffness on the control of robot manipulators

This work presents a survey of control methods applied to teleoperation systems. Although the topic is kept general, emphasis in the review is placed on force-reflecting manual controllers, which experience time delays, and suffer stability problems. Various theoretical and experimental methods that have been developed over the years are presented and the more promising ones are outlined for further study, and development of efficient, real-time control software, which is the ultimate goal of the current work.

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A review of teleoperation system control

The issue of generating a repeatable control strategy which possesses the desirable physical properties of a particular generalized inverse is addressed. The technique described is fully general and only requires a knowledge of the associated mill space of the desired inverse. While an analytical representation of the null vector is desirable, ultimately the calculations are done numerically so that a numerical knowledge of the associated full vector is sufficient. This method first characterizes repeatable strategies using a set of orthonormal basis functions to describe the null space of these transformations. The optimal repeatable inverse is then obtained by projecting the null space of the desired generalized inverse onto each of these basis functions. The resulting inverse is guaranteed to be the closest repeatable inverse to the desired inverse, in an integral norm sense, from the set of all inverses spanned by the selected basis functions. This technique is illustrated for a planar, three-degree-of-freedom manipulator and a seven-degree-of-freedom spatial manipulator. >

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Rodney G. Roberts

Papers

On the inverse kinematics, statics, and fault tolerance of cable‐suspended robots

A local measure of fault tolerance for kinematically redundant manipulators

The effect of wrist force sensor stiffness on the control of robot manipulators

A review of teleoperation system control

Repeatable generalized inverse control strategies for kinematically redundant manipulators