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

To create a secure communication among the sensor nodes, a key establishment scheme is very important. Wireless sensor networks (WSN) are usually left unattended and thus may attract the adversary to launch several attacks to the network operation. The exposure of the key during a node capture may disturb a large part of network communication. If there is a reliable, efficient, and secure KMS, disruption in the network to such an extent may not occur during a node capture attack. Several researchers have presented several key agreement schemes, but still the area is open to design an efficient attack resistant KMS. Sometimes, during the design of security protocols, the assumptions taken for the adversary behavior in sensor field may not reflect their actual behavior of the adversary in sensor field making these schemes less feasible in many real-world WSN applications. This chapter first discusses the challenges and security requirements, node capture attacks, its impact on the network, and some open issues of KMS solutions to this problem.

Key Management in WSN Security: An Attacker's Perspective

Any large scale real world Wireless Sensor Network (WSN) based application would require the sensor network to provide more features besides the energy consumption. The issues of the scalability, fault tolerance, efficient data placement as well as retrieval and real time communication are all key requirements besides energy efficiency. The existing real time approaches are not energy efficient. Moreover they are only suitable for specific types of applications and do not work well in large scale applications therefore there is a need for an effective and efficient approach which can work in any type of situation and application. In this paper, we have proposed a distributed cellular approach for our proposed real time data placement model for WSNs. It is assumed that the sensor nodes are time synchronized and aware of their locations in their deployment area. The usage of Action and Relay Stations (ARS) has been proposed for data dissemination and action in the wireless sensor network.

Real Time Approach for Data Placement Using Distributed Cellular Framework Based Clustering for Large Scale Sensor Networks

As distributed system software gain complexity owing to increasing user needs, monitoring and adaptations are necessary to keep system fit and running. These distributed applications are difficult to manage due to changing interaction patterns, behaviors and faults resulting from varying conditions in the environment. Also the rapid growth in Internet users and diverse services has highlighted the need for intelligent tools that can assist users and applications in delivering the required quality of services. To address these complexities, we introduce mobile agent based monitoring for supporting the self healing capabilities of such distributed applications. We present the novel mobile agent based monitoring technique where the monitor agents constantly collect and update the global information of the system using antecedence graphs. Updating weights of these graphs further help in evaluating host dependence and failure vulnerability of these hosts. These graphs help monitoring mobile agents to detect undesirable behaviors and also provide support for restoring the system back to normalcy.

Novel Monitoring Mechanism for Distributed System Software Using Mobile Agents

...................................................................................... ii Acknowledgements ......................................................................... iv

http://tudr.thapar.edu:8080/jspui/bitstream/10266/758/1/rbhatia_phdthesis.pdf

Design and Development of a Reusable Software Components Repository

The goal of automatic speech recognition (ASR) is to accurately and efficiently convert a speech signal into a text message independent of the device, speaker or environment. In ASR, the speech signal is captured and parameterized at front-end and evaluated at back-end using the Gaussian mixture hidden Markov model (HMM). In statistical modeling, to handle the large number of HMM state parameters and to minimize the computation overhead, similar states are tied. In this paper we present a scheme to find the degree of mixture tying that is best suited for the small amount of training data, usually available for Indian languages. In our proposed approach, perceptual linear prediction (PLP) combined with Heteroscedastic linear discriminant analysis (HLDA) was used for feature extraction. All the experiments were conducted in general field conditions and in context of Indian languages, specifically Hindi, and for Indian speaking style.

Mayank Dave

Papers

Key Management in WSN Security: An Attacker's Perspective

Real Time Approach for Data Placement Using Distributed Cellular Framework Based Clustering for Large Scale Sensor Networks

Novel Monitoring Mechanism for Distributed System Software Using Mobile Agents

Design and Development of a Reusable Software Components Repository

Tied Mixture Modeling in Hindi Speech Recognition System