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

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Preface 1. Decision-Theoretic Foundations 1.1 Game Theory, Rationality, and Intelligence 1.2 Basic Concepts of Decision Theory 1.3 Axioms 1.4 The Expected-Utility Maximization Theorem 1.5 Equivalent Representations 1.6 Bayesian Conditional-Probability Systems 1.7 Limitations of the Bayesian Model 1.8 Domination 1.9 Proofs of the Domination Theorems Exercises 2. Basic Models 2.1 Games in Extensive Form 2.2 Strategic Form and the Normal Representation 2.3 Equivalence of Strategic-Form Games 2.4 Reduced Normal Representations 2.5 Elimination of Dominated Strategies 2.6 Multiagent Representations 2.7 Common Knowledge 2.8 Bayesian Games 2.9 Modeling Games with Incomplete Information Exercises 3. Equilibria of Strategic-Form Games 3.1 Domination and Ratonalizability 3.2 Nash Equilibrium 3.3 Computing Nash Equilibria 3.4 Significance of Nash Equilibria 3.5 The Focal-Point Effect 3.6 The Decision-Analytic Approach to Games 3.7 Evolution. Resistance. and Risk Dominance 3.8 Two-Person Zero-Sum Games 3.9 Bayesian Equilibria 3.10 Purification of Randomized Strategies in Equilibria 3.11 Auctions 3.12 Proof of Existence of Equilibrium 3.13 Infinite Strategy Sets Exercises 4. Sequential Equilibria of Extensive-Form Games 4.1 Mixed Strategies and Behavioral Strategies 4.2 Equilibria in Behavioral Strategies 4.3 Sequential Rationality at Information States with Positive Probability 4.4 Consistent Beliefs and Sequential Rationality at All Information States 4.5 Computing Sequential Equilibria 4.6 Subgame-Perfect Equilibria 4.7 Games with Perfect Information 4.8 Adding Chance Events with Small Probability 4.9 Forward Induction 4.10 Voting and Binary Agendas 4.11 Technical Proofs Exercises 5. Refinements of Equilibrium in Strategic Form 5.1 Introduction 5.2 Perfect Equilibria 5.3 Existence of Perfect and Sequential Equilibria 5.4 Proper Equilibria 5.5 Persistent Equilibria 5.6 Stable Sets 01 Equilibria 5.7 Generic Properties 5.8 Conclusions Exercises 6. Games with Communication 6.1 Contracts and Correlated Strategies 6.2 Correlated Equilibria 6.3 Bayesian Games with Communication 6.4 Bayesian Collective-Choice Problems and Bayesian Bargaining Problems 6.5 Trading Problems with Linear Utility 6.6 General Participation Constraints for Bayesian Games with Contracts 6.7 Sender-Receiver Games 6.8 Acceptable and Predominant Correlated Equilibria 6.9 Communication in Extensive-Form and Multistage Games Exercises Bibliographic Note 7. Repeated Games 7.1 The Repeated Prisoners Dilemma 7.2 A General Model of Repeated Garnet 7.3 Stationary Equilibria of Repeated Games with Complete State Information and Discounting 7.4 Repeated Games with Standard Information: Examples 7.5 General Feasibility Theorems for Standard Repeated Games 7.6 Finitely Repeated Games and the Role of Initial Doubt 7.7 Imperfect Observability of Moves 7.8 Repeated Wines in Large Decentralized Groups 7.9 Repeated Games with Incomplete Information 7.10 Continuous Time 7.11 Evolutionary Simulation of Repeated Games Exercises 8. Bargaining and Cooperation in Two-Person Games 8.1 Noncooperative Foundations of Cooperative Game Theory 8.2 Two-Person Bargaining Problems and the Nash Bargaining Solution 8.3 Interpersonal Comparisons of Weighted Utility 8.4 Transferable Utility 8.5 Rational Threats 8.6 Other Bargaining Solutions 8.7 An Alternating-Offer Bargaining Game 8.8 An Alternating-Offer Game with Incomplete Information 8.9 A Discrete Alternating-Offer Game 8.10 Renegotiation Exercises 9. Coalitions in Cooperative Games 9.1 Introduction to Coalitional Analysis 9.2 Characteristic Functions with Transferable Utility 9.3 The Core 9.4 The Shapkey Value 9.5 Values with Cooperation Structures 9.6 Other Solution Concepts 9.7 Colational Games with Nontransferable Utility 9.8 Cores without Transferable Utility 9.9 Values without Transferable Utility Exercises Bibliographic Note 10. Cooperation under Uncertainty 10.1 Introduction 10.2 Concepts of Efficiency 10.3 An Example 10.4 Ex Post Inefficiency and Subsequent Oilers 10.5 Computing Incentive-Efficient Mechanisms 10.6 Inscrutability and Durability 10.7 Mechanism Selection by an Informed Principal 10.8 Neutral Bargaining Solutions 10.9 Dynamic Matching Processes with Incomplete Information Exercises Bibliography Index

博弈论 : 矛盾冲突分析 = Game theory : analysis of conflict

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Wireless sensor network (WSN) technology has promised fine grain monitoring in time and space as well as at a lower cost than is currently possible. These sensor networks are required to provide a robust service in hostile environments. Therefore the issue of real-time and reliable data delivery is extremely important for taking effective decisions in WSN. In this paper the architecture for reliable and real time approach by using sensor clusters has been proposed for storage management. Instead of storing information in an individual cluster head as suggested in some approaches, storing of information of all clusters, inside the cell is recommended within the corresponding base station. For data dissemination and action we have used Action and Relay Stations (ARS). We have developed programming model for formal specification and verification of our architecture.

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Model of Real Time Architecture for Data Placement in Wireless Sensor Networks

Problem statement: Application scenarios of Wireless Sensor Networks (WSN) create interesting design challenges since the sensor nodes used to collect and communicate data may fail suddenly and unpredictably. The issue of real-time and reliable data delivery is extremely important for taking effective decisions in WSN. Approach: In this study we proposed architecture for reliable and real-time approach for data placement using sensor clusters. Instead of storing information in individual cluster heads as suggested in some protocols, in our architecture we suggest storing of information of all clusters within a cell in the corresponding base station. For data dissemination and action we have used Action and Relay Stations (ARS). Results: We developed programming model for formal specification of our architecture. Conclusion: Present model not only offers real time sensing and monitoring, but also provides real time dynamic decision making based on the sensed data. Moreover it prolongs network life time.

Distributed Real Time Architecture for Data Placement in Wireless Sensor Networks

In a world where the next great invention is expected to appear on mobile phone screens, visually impaired people and people with Neuro-Muscular disabilities have been left behind. This paper describes a new Android application, "Morse-Comm" that improves the mobile phone experience for the visually impaired people or people with Neuro-Muscular disabilities who can barely speak by presenting a slate on which they can draw sequences of dots and dashes i.e. Morse code through "gestures" and use the application for intercommunication. The application is also used as Morse keyboard for writing documents on a personal computer. "Morse-Comm" provides better, more private and more efficient inter-communication mechanism for the blind as well as the people with Neuro-Muscular disorders.

Morse-Comm: Mobile Application Environment for Visually-Impaired and Neuro-Muscular Disabled People Using Morse Code Conversion

The design of data dissemination protocol has been a great challenge due to the highly dynamic and unreliable wireless channel in vehicular ad hoc networks (VANET). In literature, several interesting solutions are proposed to perform data dissemination for this environment. But these solutions either use architectures requiring centralised coordination or global network knowledge or large intermediate buffers. In this paper, we propose a decentralised technique that overcomes above requirements and provides reliable and scalable communication in both dense and sparse traffic for VANET. Random walks are used in the proposed technique to disseminate data from one vehicle to other vehicles in the network. We use raptor codes to provide low decoding complexity and more scalability for data dissemination. Simulation results demonstrate that the proposed technique has better fault tolerance with lower complexity than general random-walk-based dissemination process and more scalability as compared to the other p...

DDDRC: decentralised data dissemination in VANET using raptor codes

There is an increasing research interest in solving routing problems in Wireless Sensor Networks (WSN) and most of the existing routing protocols for WSN consider homogeneous nodes wherein, all sensor nodes have the same capabilities in terms of sensing, communication and computation capabilities. However, deployment of a homogeneous sensor network may suffer from poor performance. In this paper, we present a novel QoS routing scheme that adaptively learns an optimal routing strategy, depending on multiple optimization goals. We base our approach on Ant Colony Optimization (ACO) technique which is insensitive to initial setting, thus ideal in the context of WHSNs. The key feature of the protocol is its ability to meet diverse QoS requirements posed by different kinds of traffic generated due to heterogeneous nature of nodes thus maximizing network performance and utilization. Experimental results suggest a significant gain in the performance of proposed protocol over AODV protocol.

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Mayank Dave

Papers

Model of Real Time Architecture for Data Placement in Wireless Sensor Networks

Distributed Real Time Architecture for Data Placement in Wireless Sensor Networks

Morse-Comm: Mobile Application Environment for Visually-Impaired and Neuro-Muscular Disabled People Using Morse Code Conversion

DDDRC: decentralised data dissemination in VANET using raptor codes

QoS Enabled Probabilistic Routing for Heterogeneous Wireless Sensor Networks