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

Ways of improving the accuracy and efficiency of automatic speech recognition (ASR) systems have been a long term goal of researchers to develop the natural language man machine communication interface. In widely used statistical framework of ASR, feature extraction technique is used at the frontend for speech signal parameterization, and hidden Markov model (HMM) is used at the back-end for pattern classification. This chapter reviews classical and recent approaches of Markov modeling, and also presents an empirical study of few well known methods in the context of Hindi speech recognition system. Various performance issues such as number of Gaussian mixtures, tied states, and feature reduction procedures are also analyzed for medium size vocabulary. The experimental results show that using advanced techniques of acoustic models, more than 90% accuracy can be achieved. The recent advanced models outperform the conventional methods and fit for HCI applications.

Recent Trends in Speech Recognition Systems

Most data aggregation and dissemination techniques for Vehicular Ad Hoc Networks (VANETs) attempt to create and utilize a structure for collecting information. Conventional data aggregation schemes are not appropriate for mobile network. In this paper, we design a cooperative model to facilitate the aggregation of adjacent traffic reports. The idea behind this work is that we can adaptively change the forwarding delay of individual reports in a manner that a report can have a better chance to meet other reports. Simulation results based on realistic map data and traffic models demonstrate that proposed scheme can effectively reduce communication overhead with acceptable delay.

Knowledge Based Framework for Data Aggregation in Vehicular Ad Hoc Networks

Content Centric Networking (CCN) is a new networking architecture aims to address current challenges faced by TCP/IP architecture. Motivation behind idea of CCN is to avoid repeated delivery of same content (information). CCN avoids sending of same content again and again by caching the sending content at some intermediate node (router). In CCN architecture each node has storage capacity to cache the propagating content. By caching content at intermediate nodes, CCN stops requests to reach to original server of that content. The intermediate node, having requested content in its cache, can directly serve further requests for same content, acting as server. Hence all requests not necessarily reach to original server and that's how considerable amount of bandwidth is saved. To cache content, there is need of algorithm to select subset of nodes among available, to serve new incoming requests for longer time. In our research work we proposed an Cost Effective Caching algorithm which selects eligible node based on their remaining capacity left to cache more content. The main motivation behind proposing this algorithm is to cache content at a node which has enough storage left to cache the content. We implemented and compared performance of this scheme with respect to existing ones and results are showing that this algorithm significantly improves performance of network.

Cost Effective Caching in Content Centric Networking

The Current description standards for Web Services such as WSDL and UDDI have a significant drawback of being restricted to the syntactic aspects of service. A service provider registers a service in the universal repository i.e. UDDI so that the service consumers can search and discover the required service that meets the user functional requirements from thousands of registered services. Matching the user request with all services in a particular category of the repository is a cumbersome task. Semantic approaches are required to further assist the user in discovering relevant services. In this paper, we have proposed a semantic approach that gives ranked list of services based on the web based relatedness score and helps the users in the selection of potentially relevant and semantically similar services within a category. The proposed approach has been implemented on 80 OWLS services and the results have shown that the approach gives ranked list of services with ease of the selection process for the user.

Google based hybrid approach for discovering services

The practical challenges on the web are irrelevant and huge number of services returned by the UDDI and lack of standard mechanisms that helps in the discovery of desired web services. The utilization of the implicit semantic information from the service profiles can help the service consumers in selecting the most relevant services from a set of offered services. In this paper, an approach for web service discovery is proposed which uses a lexical semantic network constructed from the web snippets as a knowledge base for the calculation of semantic similarity between the service profiles. Our approach takes into account the text descriptions and involves mapping of service profiles to a category based dimension vector by using the notion of semantic similarity which is further merged with the IR based techniques of weight generation and is used for calculating the semantic degree of similarity between the services. We present results that we obtained by applying the approach on set of 106 OWL-S service profiles. Empirical evaluation shows that the proposed approach helps in better discovery of semantically similar and relevant services which are otherwise shown to be unrelated by the keyword based approaches.

Mayank Dave

Papers

Recent Trends in Speech Recognition Systems

Knowledge Based Framework for Data Aggregation in Vehicular Ad Hoc Networks

Cost Effective Caching in Content Centric Networking

Google based hybrid approach for discovering services

Normalized similarity based semantic approach for discovery of web services