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

In structured and unstructured Peer-to-Peer P2P systems, frequent joining and leaving of peer nodes causes topology mismatch between the P2P logical overlay network and the physical underlay network. This topology mismatch problem generates high volumes of redundant traffic in the network. This paper presents Common Junction Methodology CJM to reduce network overhead by optimize the overlay traffic at underlay level. CJM finds common junction between available paths, and traffic is only routed through the common junction and not through the conventional identified paths. CJM does not alter overlay topology and performs without affecting the search scope of the network. Simulation results show that CJM resolves the mismatch problem and significantly reduces redundant P2P traffic up to 87% in the best case for the simulated network. CJM can be implemented over structured or unstructured P2P networks, and also reduces the response time by 53% approximately for the network.

Reducing Network Overhead with Common Junction Methodology

Automatic or semiautomatic categorization of web services facilitates the relevant service retrieval as well as it helps the administrators in attaining globally consistent classification decisions that are independent of the administrator’s knowledge of application domain, organization of taxonomies and service characteristics. Lack of automatic mechanisms to help service publishers in the classification task, irrelevant and huge number of services returned by the UDDI, and lack of standard mechanisms that helps in the discovery of desired web services are some of the issues that need focus. In this paper, an automatic approach for service categorization is proposed that uses a lexical semantic network constructed from the web snippets as a knowledge base for semantic similarity calculation between the service profiles and the categories. Our approach involves mapping of service profiles to a category based dimension vector by using the notion of semantic similarity and aims at alleviating the administrator’s job by automatically providing them with a set of categories ranked based on the degree of semantic similarity. Empirical evaluation on a set of OWL-S services shows that the proposed approach helps in better decisions for relevant classification of services by giving an ordered set of categories based on the similarity scores.

Semantic Approach for Classification of Web Services Using Unsupervised Normalized Similarity Measure

Speech recognition (i.e. speech to text conversion)and speech synthesis (i.e. text to speech conversion)are two main operations performed in human computer interaction through natural language conversational interface. This paper presents a novel approach for modeling and designing of a Hindi speech recognition system, by using Perceptual Linear Prediction (PLPRASTA)for feature extraction and Gaussian Mixture Model (GMM) for statistical pattern classification. Experimental results show that only 4 Gaussian mixtures yield optimal performance in the context of small databases available for Indian languages which have been used to train the Hidden Markov model (HMM). The results also illustrate that the approach presented in this paper, not only outperform the traditional model for small vocabulary in typical field conditions but can also be implemented efficiently in embedded systems.

Fitness Evaluation of Gaussian Mixtures in Hindi Speech Recognition System

The inter-actor connectivity is absolutely crucial in wireless sensor and actor networks for timely responses to events generated in the network Due to the sparse nature of the network, the failure of one or more actor nodes results in network partitioning problem very frequently To provide effective connectivity between disjoint segments, a self-healing solution is required with a small number of involved actor nodes Also, their travelling distances should be small To find stable actor nodes with their small moving distance for recovery is an NP-hard problem for large scale problems However, the present literature requires broadcasting of route request packets to find the nodes or location for recovery, which consumes more network energy and consequently decreases the network lifetime In this study, we propose an algorithm called Node Stability aware Connectivity Restoration Algorithm (NSCRA) to tackle such network partitioning problem in an energy efficient way with stable actor nodes The stability of node(s) is calculated by using controlled packet forwarding probability (ie node route request) that is based upon neighbour energy decay rate The selection of node is based on its backup power to avoid further network partitioning (ie overlapped or collocated failures) during recovery Our proposed approach performs not only energy efficient recovery due to the participation of stable nodes, but also reduces the message overhead of the network The experimental simulation results show that proposed approach outperforms other existing state-of-the-art approaches

Node Stability Aware Energy Efficient Single Node Failure Recovery Approach for WSANs

For the last two decades, research in the field of automatic speech recognition (ASR) has been intensively carried out worldwide, motivated by the advances in signal processing techniques, pattern recognition algorithms, computational resources and storage capability. Most state-of–the–art speech recognition systems are based on the principles of statistical pattern recognition. In such systems, the speech signal is captured and preprocessed at front-end for feature extraction and evaluated at back-end using continuous density hidden Markov model (CDHMM). Maximum likelihood estimation (MLE) and several discriminative training methods have been used to train the ASR, based on European languages such as English. This paper reviews the existing discriminative techniques like maximum mutual information estimation (MMIE), minimum classification error (MCE), and minimum phone error (MPE), and presents a comparative study in the context of Hindi language ASR. The system is speaker independent and works with medium size vocabulary in typical field conditions.

Mayank Dave

Papers

Reducing Network Overhead with Common Junction Methodology

Semantic Approach for Classification of Web Services Using Unsupervised Normalized Similarity Measure

Fitness Evaluation of Gaussian Mixtures in Hindi Speech Recognition System

Node Stability Aware Energy Efficient Single Node Failure Recovery Approach for WSANs

Discriminative Techniques for Hindi Speech Recognition System