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

Trust model for cluster head validation in underwater wireless sensor networks

The flexibility offered by mobile agents is quite noticeable in distributed computing environments. However, the greater flexibility of the mobile agent paradigm compared to the client/server computing paradigm comes at an additional threats since agent systems are prone to failures originating from bad communication, security attacks, agent server crashes, system resources unavailability, network congestion, or even deadlock situations. In such events, mobile agents either get lost or damaged (partially or totally) during execution. In this paper, we propose parallel checkpointing approach based on the use of antecedence graphs for providing fault tolerance in mobile agent systems. During normal computation message transmission, the dependency information among mobile agents is recorded in the form of antecedence graphs by participating mobile agents of mobile agent group. When a checkpointing procedure begins, the initiator concurrently informs relevant mobile agents, which minimizes the identifying time. The proposed scheme utilizes the checkpointed information for fault tolerance which is stored in form of antecedence graphs. In case of failures, using checkpointed information, the antecedence graphs and message logs are regenerated for recovery and then normal operation continued. Moreover, compared with the existing schemes, our algorithm involves the minimum number of mobile agents during the identifying and checkpoiting procedure, which leads to the improvement of the system performance. In addition, the proposed algorithm is a domino-free checkpointing algorithm, which is especially desirable for mobile agent systems. Quantitative analysis and experimental simulation show that our algorithm outperforms other coordinated checkpointing schemes in terms of the identifying time and the number of blocked mobile agents and then can provide a better system performance. The main contribution of the proposed checkpointing scheme is the enhancement of graph-based approach in terms of considerable improvement by reducing message overhead, execution, and recovery times.

Antecedence Graph Approach to Checkpointing for Fault Tolerance in Mobile Agent Systems

Software-defined networking (SDN) is an advanced networking technology that yields flexibility with cost-efficiency as per the business requirements. SDN breaks the vertical integration of control and data plane and promotes centralized network management. SDN allows data intensive applications to work more efficiently by making the network dynamically configurable. With the growing development of SDN technology, the issue of security becomes critical because of its architectural characteristics. Currently, Distributed denial of service (DDoS) is one of the most powerful attacks that cause the services to be unavailable for normal users. DDoS seeks to consume the resources of the SDN controller with the intention to slow down working of the network. In this paper, a detailed analysis of the effect of spoofed and non-spoofed TCP-SYN flooding attacks on the controller resources in SDN is presented. We also suggest a machine learning based intrusion detection system. Five different classification models belong to a variety of families are used to classify the traffic, and evaluated using different performance indicators. Cross-validation technique is used to validate the classification models. This work enables better features to be extracted and classify the traffic efficiently. The experimental results reveal significantly good performance with all the considered classification models.

Detection and Analysis of TCP-SYN DDoS Attack in Software-Defined Networking

In this paper, we address the problem of physical node capture attack from an adversarial view. In this type of attack, an adversary intelligently captures the node(s) and extracts the cryptographic information to destroy the privacy, safety and reliability of the network. We propose a novel hybrid approach for constructing the path vulnerability matrix that maximizes the attacking efficiency and lowers the resource expenditure of the adversary. The main strength of the proposed matrix lies in incorporating various criteria such as the type of application running on the nodes, capturing cost, key dominance ranking, articulation point and intersecting shortest path nodes in estimating the vulnerability. The location of the data sink is considered as a major factor in estimating the vulnerability of the nodes in the network. The proposed matrix is compared with existing attacking strategies in terms of the number of attacking rounds, energy cost and fraction of compromised traffic. Although in this paper, we have considered adversarial point of view but the proposed matrix can be extended for the defender application also. The path vulnerability matrix provides vulnerability estimation that can be used to analyze system security weakness and helps to defend against them effectively and efficiently. The performance of proposed approach has been validated with respect to average path compromise ratio, average routing path length and average route successful ratio. We have also introduced an attack resistant random key predistribution scheme based on the proposed approach and the results confirm its effectiveness in decreasing node capture impact.

A Hybrid Approach for Path Vulnerability Matrix on Random Key Predistribution for Wireless Sensor Networks

Most of the existing VANET routing protocols rely on information collected from beacons for making routing decisions such as next neighbor selection. Beacons are very small size hello messages that each vehicle broadcasts periodically. Owing to very small payload size of beacons as compared to the payload size of a data message, they can easily pass through even very weak links, through which a data message could never pass. Therefore, the use of beacon information for making routing decisions in a highly dynamic scenario such as VANETs may cause selection of routes through which data message can never be sent. Several researchers have given solutions that do not use beacon information for making routing decisions. But most of these solutions incorporate a large number of broadcasts to forward the data packets, causing wastage of bandwidth. In this paper, we present a beacon information independent geographic routing algorithm called BIIR, which reduces the number of broadcasts to forward the data packets by making intelligent use of information collected by the vehicle during previous route discovery attempts for a destination. Our simulation results have shown that the proposed algorithm outperforms the existing beacon less routing protocols in terms of the average number of broadcasts per data packet forwarding, packet delivery ratio and end to end delay experienced by the data messages.

Mayank Dave

Papers

Trust model for cluster head validation in underwater wireless sensor networks

Antecedence Graph Approach to Checkpointing for Fault Tolerance in Mobile Agent Systems

Detection and Analysis of TCP-SYN DDoS Attack in Software-Defined Networking

A Hybrid Approach for Path Vulnerability Matrix on Random Key Predistribution for Wireless Sensor Networks

BIIR: A Beacon Information Independent VANET Routing Algorithm with Low Broadcast Overhead