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 networks (WSNs) have become a very active and important area of research due to their potential in civil and military applications. A lot of research works focus on energy efficient routing protocols due to the limited energy of battery-powered nodes. Resource constraints on nodes in the network coupled with considerable redundancy in the data generated mean that applications have to be developed with an eye to maximizing energy efficiency in order to extend network life. Innetwork computation, and in particular innetwork information extraction from data, has been promoted as a technique for achieving this aim. In-network processing, however, has been limited to systems where at most, simple aggregate queries are evaluated, the results of which are communicated to the outside world. The main challenge in wireless sensor networks is to produce energy efficient networks. In this article, we have explored various approaches involved in query processing done by sensor nodes which have limited power and we analyzed region based approach including the region set up cost in consideration which depicts that with increasing the number of divisions of the regions, the number of messages communicated were decreased. The simulation result shows the considerable reduction in power consumption and hence increases the life of sensor.

Simulation of Power Efficient Region Based Approach for Query Processing in Wireless Sensor Networks

Organizations across the world are using Cloud to leverage some of its key aspects, such as reduced startup costs, data transmission expenditures, pay-as-you-use model etc. Increasing usage of Cloud based services not only raises conventional issues related to data security and privacy, but also causes handling data ownership and handling it dynamically difficult. This has now become a prominent research topic. To support real-time applications, concept of Fog computing have emerged. In this paper, we propose a model utilizing Fog computing and incorporating dynamic data ownership for data aggregation and deduplication. The technique presented in the paper uses Key Aggregate Cryptography and Bilinear groups.

Key Aggregate Cryptography Based Solution for Dynamic Ownership Management in Fog Infrastructures

In recent years many researchers are incorporating the mobile agents in e-service applications especially in e-learning and e-commerce to improve the network latency and to reduce the network traffic. On the other side, the security issues degrade the mobile agent usage. The main intention of the attacker is to kill or modify the behaviour of the agent in the middle of the journey to degrade the trustiness of the agent environment. In this paper, we propose fault tolerance mechanism for preventing the agent blocking in scenarios where the agent is captured by malicious host in the network. This approach makes use of acknowledgements and partial result retrieval and when implemented in mobile agent platform allows the originator to retrieve partial results and track the location of mobile agent at any time during the process of transaction execution. During the recovery of the mobile agent all the components (agent code, itinerary, credential information, collected information and state) are able to recover. The proposed mechanism is capable of improving fault tolerant time, reliability and performance, especially for mobile agents in e-commerce Internet applications.

Platform independent non blocking mechanism for prevention of blocking attacks in mobile agents based e-service applications

The ever growing number of Internet subscribers through mobile devices has led to the requirement of better mobility management protocols. Mobile IP version 6 (MIPv6) by IETF supports IPv6 mobility by providing mobile nodes with uninterrupted Internet connectivity and services. MIPv6 provides network layer mobility but handoff from one network to another network causes handoff latency. Due to this latency some packets may be lost or delayed in transfer affecting the performance of transport layer. In this paper, we present a low-latency handoff scheme for MIPv6. The scheme requires access point to facilitate movement detection and access router to implement tentative addresses pool and neighbor cache table for faster address configuration with faster duplicate address detection. The performance analysis shows that proposed scheme provides with lower latency and packet loss as well as improved signaling cost and packet delivery cost.

Mayank Dave

Papers

Simulation of Power Efficient Region Based Approach for Query Processing in Wireless Sensor Networks

Key Aggregate Cryptography Based Solution for Dynamic Ownership Management in Fog Infrastructures

Platform independent non blocking mechanism for prevention of blocking attacks in mobile agents based e-service applications

Optimizing handoff latency for mobile IPv6 based environments

Mitigation of DDoS Attack Using Moving Target Defense in SDN