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

The recent advancements in the game theory have led to it being applied in various applications such as communication, networks, business, biology, political system etc. Precisely, Max-Min Algorithm is a decision based rule used in the game theory for deciding the next step of a player out of a set of possible steps. It can be thought of maximizing the minimum profit of the player. The assumption made in the current literature of zero-sum game theory is that both players are rational and logical to decide the best possible step out of the available options. On the Prima Facie, we expect a player to choose the best possible step for himself/herself. But in doing so, he/she might give away his/her move to his/her rival, who, being a rational thinker, can manipulate the game to take his/her advantage or alternatively rival's loss. Our proposed approach seeks to overcome this loophole presented in the current Max-Min approach by construction of a function which solves the trade-off between predictability and maximum profit. The key idea here is to select a step with a potential to earn high profit and being unpredictable in picking up that step at the same moment, thus making it nearly impossible for the adversary to predict the next step. In the nut shell, our work is an attempt to reduce the worst case complexity of original Max-Min approach.

Modified Max-Min Algorithm for Game Theory

Structured query language (SQL) injection is an attack method that explores the functional and storage vulnerabilities of web applications that have data stored in a database. The attacker is capable of affecting the security by intentionally deciding the content that will be forwarded to the database for information retrieval. The attacker gets the benefit by exploiting the syntax and storage vulnerabilities that are responsible for weak points generated in the DBMS security system. This study makes use of the Snort intrusion detection system log files that contain information affiliated to attackers and can provide timed attack notifications via digital notification systems, like emails. In this research, a web server-based network system is initialized using the Snort intrusion detection system (IDS) to detect various methods of SQL injection attacks possible. The method used is based on NIST standards which are based on major risk assessment phases. This is a five phase-based research that performs exploit site testing, simulating attack circumstances, configuring IDS, collecting data and final phase of performing analysis. This study contributes to a web server-based IDS snort system that is capable of detecting a significant number of SQL injection attacks and real-time response notifying system via digital notifications.

SQL Injection Attack Detection, Evidence Collection, and Notifying System Using Standard Intrusion Detection System in Network Forensics

Congestion and quality of service are widely researched topics in Wireless Sensor Networks in recent years. Many researchers proposed and compared the merits and demerits of various algorithms with the existing algorithms. The major challenge lies in developing an algorithm which optimizes the various performance parameters like packet drop ratio, residual energy and throughput of the network. Focus of the present work is to reduce congestion and improve quality of service by applying various metaheuristic or computational intelligence techniques which can optimize performance parameters. An objective function is formulated on the basis of factors like residual energy, throughput, distance between nodes and the number of retransmissions and its value is optimized by using various nature inspired computational intelligence techniques and their results are compared. Simulation results have shown that water wave algorithm outperforms all the other algorithms on the basis of packet drop ratio and throughput of wireless sensor network.

Computational Intelligence for Congestion Control and Quality of Service Improvement in Wireless Sensor Networks

With the growing stress on today's power system, it is operated much closer to its stability limit. Under such circumstances it is highly desirable that one must be able to assess the security and stability of the electric power system when exposed to disturbances/faults. In the post-fault transient analysis of interconnected systems, the transient energy margin which is a complex function of prefault system conditions, structure of fault (type and location) and network topology at the specified fault clearing time gives a quantitative idea about the stability of the system. High adaptation capabilities of artificial neural networks make them capable of synthesizing the complex mapping that transform the input features in to a single-valued space of energy margin. Appropriate input feature selection has a direct bearing on the consistency and accuracy of mapping. This issue has been addressed in the present paper by comparing the prediction results based on approaches (Sobajic and Pao, 1989), (So...

Input-features based comparative study of intelligent transient stability assessment

Mobile agents are software programs that may move from host to host as necessary to carry out their functions. Such systems violate some of the assumptions that underlie most existing computer security implementations. Full-scale adoption of mobile agent technology in untrustworthy network environment, such as Internet, has been delayed due to several complexities. Problem of Malicious Host (POMH) (i.e. how to protect agents against the malicious hosts) is a serious security problem in mobile agent system. The protection of mobile agents against the attacks of malicious hosts is considered a very challenging security problem. It has inspired lot of research interest, but very few measures exist to counter blocking attack where a host with malicious intentions refuses to transmit a mobile agent to the next host. It is significant and important requirement of the agent owner to rescue the data collected by the agent under custody and redeem a loss. In this paper, we present our scheme that aims to rescue the results being carried by blocked agent. The significant contribution of the scheme is the real time applicability in mobile agent based applications and maximum reduction of data loss in case of blocking attacks.

Mayank Dave

Papers

Modified Max-Min Algorithm for Game Theory

SQL Injection Attack Detection, Evidence Collection, and Notifying System Using Standard Intrusion Detection System in Network Forensics

Computational Intelligence for Congestion Control and Quality of Service Improvement in Wireless Sensor Networks

Input-features based comparative study of intelligent transient stability assessment

Rescuing data of mobile agents blocked by malicious hosts in e-service applications