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

Preface 1. Introduction Overview A Brief History of LMIs in Control Theory Notes on the Style of the Book Origin of the Book 2. Some Standard Problems Involving LMIs. Linear Matrix Inequalities Some Standard Problems Ellipsoid Algorithm Interior-Point Methods Strict and Nonstrict LMIs Miscellaneous Results on Matrix Inequalities Some LMI Problems with Analytic Solutions 3. Some Matrix Problems. Minimizing Condition Number by Scaling Minimizing Condition Number of a Positive-Definite Matrix Minimizing Norm by Scaling Rescaling a Matrix Positive-Definite Matrix Completion Problems Quadratic Approximation of a Polytopic Norm Ellipsoidal Approximation 4. Linear Differential Inclusions. Differential Inclusions Some Specific LDIs Nonlinear System Analysis via LDIs 5. Analysis of LDIs: State Properties. Quadratic Stability Invariant Ellipsoids 6. Analysis of LDIs: Input/Output Properties. Input-to-State Properties State-to-Output Properties Input-to-Output Properties 7. State-Feedback Synthesis for LDIs. Static State-Feedback Controllers State Properties Input-to-State Properties State-to-Output Properties Input-to-Output Properties Observer-Based Controllers for Nonlinear Systems 8. Lure and Multiplier Methods. Analysis of Lure Systems Integral Quadratic Constraints Multipliers for Systems with Unknown Parameters 9. Systems with Multiplicative Noise. Analysis of Systems with Multiplicative Noise State-Feedback Synthesis 10. Miscellaneous Problems. Optimization over an Affine Family of Linear Systems Analysis of Systems with LTI Perturbations Positive Orthant Stabilizability Linear Systems with Delays Interpolation Problems The Inverse Problem of Optimal Control System Realization Problems Multi-Criterion LQG Nonconvex Multi-Criterion Quadratic Problems Notation List of Acronyms Bibliography Index.

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Linear Matrix Inequalities in System and Control Theory

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

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Convex Analysisの二,三の進展について

The Theory of Matrices. By F R. Gantmacher. Two volumes, pp. 374 and 276. 1959. (Translated from the Russian by K. A. Hirsch; Chelsea Publishing Company, New York)

The problem of the stabilization of a 'minimum phase' single-input single-output (SISO) linear time-varying system is discussed. A sufficient condition is given for the exponential stability of two interconnected linear time-varying systems. A technique is suggested to design a time-invariant compensator that guarantees exponential stability of the closed loop system. >

Stabilization of a class of time-varying SISO plants by time-invariant compensators

Voice is the result of the coordination of the whole pneumophonoarticulatory apparatus. The analysis of the voice allows the identification of the diseases of the vocal apparatus and currently is carried out from an expert doctor through methods based on the auditory analysis. This paper presents a web-based system for the acquisition and automatic analysis of vocal signals. Vocal signals are submitted by the users through a simple web-interface and are analyzed in real-time by using state-of-the art signal processing techniques, providing first-level information on possible voice alterations. The system offers different analysis functions to the doctors that may analyze suspected cases in detail. The system is currently being tested in the otorhinolaryngologist setting to carry out mass prevention via screening at a regional scale.

A web-based system for the collection and analysis of spectra signals for early detection of voice alterations

In this paper we deal with the finite-time stability problem for quadratic systems. Such class of systems plays an important role in the modeling of a wide class of nonlinear processes (electrical, robotic, biological, etc.). The main results of the paper consist of two sufficient conditions for finite-time stability analysis and finite-time stabilization via static state feedback; both conditions are given in terms of the feasibility of a convex optimization problem, involving linear matrix inequalities. A numerical example illustrates the applicability of the proposed technique.

Sufficient conditions for finite-time stability and stabilization of nonlinear quadratic systems

In the current clinical practice, the diagnosis of Rheumatoid Arthritis (RA) draws on the qualitative evaluation of pain sensitivity thresholds which is affected by several source of uncertainties, due to an operator-dependent diagnostic protocol. Taking our cue from the diagnostic shortcomings, we have explored the possibility of automating the measurement of mechanical nociceptive thresholds through the adoption of soft pneumatic actuators controlled by an embedded control unit. In this work, we want to show that a purpose-made biomechatronic device actuated by soft and pneumatic actuators is potentially a boon both for rheumatologists and biomedical researchers involved in nociception and physicophysical studies. In the full breadth and scope of the objective diagnosis of RA, the first prototype of a novel biomechatronic device for quantitative and automatic measurement of mechanical nociceptive thresholds has been designed and tested through nociception experiments on 10 subjects. The experimental results show that the designed device can reliably generate the controllable and repeatable nociceptive stimuli needed for the objective diagnosis of RA.

A Biomechatronic Device Actuated by Pneumatic Artificial Muscles for the Automatic Evaluation of Nociceptive Thresholds in Rheumatic Patients

The increasing number of novel theoretical and numerical tools developed in the field of systems biology requires more and more quantitative data and system-level knowledge. On the other hand, while biotechnologies have greatly evolved during the last decade, the time and cost required for experimental measurements, especially in the case of time-series data, are still rather high. In-silico models can overcome these drawbacks, provided they are realistic enough to produce valuable experimental data useful to test and validate reverse engineering algorithms. In the present work, a novel approach for the generation of random in-silico models of biological interaction systems is proposed. Interaction network models are automatically generated by means of cellular automata and properties common to real biological networks are reproduced as emergent properties of complex systems.

Francesco Amato

Papers

Stabilization of a class of time-varying SISO plants by time-invariant compensators

A web-based system for the collection and analysis of spectra signals for early detection of voice alterations

Sufficient conditions for finite-time stability and stabilization of nonlinear quadratic systems

A Biomechatronic Device Actuated by Pneumatic Artificial Muscles for the Automatic Evaluation of Nociceptive Thresholds in Rheumatic Patients

Emergence of system-level properties in biological networks from cellular automata evolution