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

Now a days, Generative Adversarial Networks (GANs) are an arising technology for both supervised and unsupervised learning which have capability to generate data of high standard. Image to Image translation is one of the application of GANs as a data augmentation which we have used in this proposed framework. Generative Networks makes the mapping between source image and target image easier and it calculates the loss function also to improve the quality of generated target image. In this paper, Conditional GANs are used which translates the images based upon some conditions. The performance is also analyzed of the model by doing hyper-parameter tuning.

Image-to-Image Translation Using Generative Adversarial Network

State-of-the-art automatic speech recognition (ASR) systems follow a well established statistical paradigm, that of parameterization of speech signals (a.k.a. feature extraction) at front-end and likelihood evaluation of feature vectors at back-end. For feature extraction, Mel-frequency cepstral coefficients (MFCC) and perceptual linear prediction (PLP) are the two dominant signal processing methods, which have been used mainly in ASR. Although the effects of both techniques have been analyzed individually, it is not known whether any combination of the two can produce an improvement in the recognition accuracy or not. This paper presents an investigation on the possibility to integrate different types of features such as MFCC, PLP and gravity centroids to improve the performance of ASR in the context of Hindi language. Our experimental results show a significant improvement in case of such few combinations when applied to medium size lexicons in typical field conditions.

https://link.springer.com/content/pdf/10.1007%2Fs11235-011-9623-0.pdf

Performance evaluation of sequentially combined heterogeneous feature streams for Hindi speech recognition system

Recently, with the explosive growth of Information and Communication Technologies (ICT), various new opportunities emerged for the creation and delivery of content in digital form which includes applications such as real time video and audio delivery, electronic advertising, digital libraries and web publishing. However, these advantages have the consequent risks of data piracy, which motivate towards development of new protection mechanisms. One such effort that has been attracting interest is based on digital watermarking techniques. This chapter discusses the basic concepts of digital watermarking techniques, performances parameters and its potential applications in various fields. In this chapter, we also discuss various spatial and transform domain techniques and compare the performance of some reported wavelet based watermarking techniques. Finally, the latest applications of watermarking techniques have been discussed. This chapter will be more important for researchers to implement effective watermarking method.

Digital Image Watermarking: Techniques and Emerging Applications

In automatic speech recognition (ASR) systems, hidden Markov models (HMMs) have been widely used for modeling the temporal speech signal. As discussed in Part I, the conventional acoustic models used for ASR have many drawbacks like weak duration modeling and poor discrimination. This paper (Part II) presents a review on the techniques which have been proposed in literature for the refinements of standard HMM methods to cope with their limitations. Current advancements related to this topic are also outlined. The approaches emphasized in this part of review are connectionist approach, explicit duration modeling, discriminative training and margin based estimation methods. Further, various challenges and performance issues such as environmental variability, tied mixture modeling, and handling of distant speech signals are analyzed along with the directions for future research.

Acoustic modeling problem for automatic speech recognition system: advances and refinements (Part II)

The inter-actor connectivity is a very crucial issue to maintain network operation in the wireless sensor and actor networks. Most of the applications have been proposed for harsh environments where the backbone actor nodes are prone to failure or get damaged due to their battery power exhaustion or get physically damaged. Such failures can partition the network due to failure of the cut-vertex node and eventually decrease the network performance or even sometimes make the network useless. Currently, a few approaches have been proposed to restore the partitioned network due to failure of the cut-vertex node but without considering the recovery node capabilities. This paper proposes a localized hybrid timer based cut-vertex node failure recovery approach called distributed prioritized connectivity restoration algorithm (DPCRA) to handle such partitions and restore connectivity with the help of a small number of nodes. The main idea is to proactively identify whether the failure of an actor node causes partition or not in the network. If partition occurs the designated failure handlers (FHs) detect that partition and repair it locally using minimum information stored in each actor node. In case first designated node is unable to start the recovery process within a permissible reaction time the next designated FH could start the recovery process. The main strength of our paper is the use of multiple backup nodes for the guaranteed partitioned recovery. The experimental simulation shows that DPCRA outperforms other existing state-of-the-art approaches in terms of the number of participated repairing nodes and their total moving distance for the recovery to restore the disconnected partitions.

Mayank Dave

Papers

Image-to-Image Translation Using Generative Adversarial Network

Performance evaluation of sequentially combined heterogeneous feature streams for Hindi speech recognition system

Digital Image Watermarking: Techniques and Emerging Applications

Acoustic modeling problem for automatic speech recognition system: advances and refinements (Part II)

A Hybrid Timer Based Single Node Failure Recovery Approach for WSANs