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

IQR-based approach for DDoS detection and mitigation in SDN

Hybrid Watermarking Approach is one of the trending approaches in the field of information security. Watermarking has a variety of applications to ensure data authentication, security, verification, and copyrights protection of digital media. The proposed approach is hybrid watermarking applied on image and audio. We proposed a scheme in which the image is blindly embedded over original data with the help of Discrete Wavelength Transformation(DWT) and Singular value decomposition(SVD). Now the watermarked image is embedded into an audio signal by taking advantage of the histogram by making a gathering of three neighboring bins having various samples into it and later histogram is pulled out from embedding range by quoting to the modified mean. There is appropriate security analysis of the hybrid watermarking with respect to robustness and imperceptibility mainly against synchronization attacks and we also analyze the robustness in the case of common attacks

Robust Hybrid Watermarking using Image and Audio against Synchronization Attacks



Only a fraction of the produced social media data is usable in mental health assessment. So the problem of sufficient training data for deep learning approaches arises. Data sufficiency can be presented in terms of number of users or number of posts per user.



We examine the data need of machine learning and deep learning models for a practical system and let researcher chose best fitting models depending on the dataset type available with them. We perform distinct experiments to find the effect of these issues on depression classification by various approaches.



We explored various machine learning and deep learning techniques on various data set versions, taken from Twitter and Reddit, with varying numbers of users and posts per user. Diagnosed and control users are taken in different ratios to assess the impact of an imbalanced dataset.



The results reveal that SVM achieved 68% accuracy in depression classification for 70 users each from diagnosed and control group. It decreases for 150 users from each group, but then regains performance for 350 and 550 users from each group.



Conclusion: We found, mainly, classification accuracy increases with the number of users, number of posts per user and imbalance in the number of diagnosed versus control users. We also found that posts from Reddit have better accuracy compared to tweets.


An investigation of data requirements for detection of depression from social media posts

Software-defined networking (SDN) has become a popular networking paradigm that provides a separation of control plane from the data plane devices. This separation offers various facilities such as flexibility, programmability, and a centralized global view of the complete network. However, SDN introduces various cyber-attacks due to its architectural design. In the present scenario, Distributed denial-of-service (DDoS) is one of the most severe attacks that makes the resources unavailable for normal users. Attackers use spoofed IPs in the DDoS attack and hide their original identity, which making it difficult to mitigate the impacts of such kind of DDoS attacks. An spoofed DDoS attack in SDN can affect all three important entities, such as switch, controller, and switch-controller channel. Therefore, in this work, we propose an statistical measure - z-score based mechanism against spoofed flooding DDoS attack (TCP-SYN) for attack detection. The proposed mechanism also mitigates the impacts of the attacks and avoids any additional communication between switches and controller. The performance results show that the proposed attack defense mechanism mitigates the spoofed TCP-SYN flooding attack efficiently and reduces the CPU usage on the victim host after attack detection.

Addressing Spoofed DDoS Attacks in Software-defined Networking

Today an average transmission line is loaded more heavily than ever before, and this has given rise to a serious problem of voltage instability. The authors present a novel method for power system voltage instability estimation and improvement using ANN. The method is based on the fact that reactive power injections at critical buses of the power system help to steer the system away from a developing voltage collapse. The location and quantum of reactive power support has been computed based on sensitivity. The sensitivity matrix is formulated by cascading the output/input sensitivity of multiplayer perceptron model with that of input features versus reactive power injections. The effectiveness of the proposed method is demonstrated on Ward-Hale 6-bus, IEEE 14-bus, and IEEE 30-bus test systems. The method can be effectively used to make the system secure against voltage collapse condition in system planning and online operation.

Mayank Dave

Papers

IQR-based approach for DDoS detection and mitigation in SDN

Robust Hybrid Watermarking using Image and Audio against Synchronization Attacks

An investigation of data requirements for detection of depression from social media posts

Addressing Spoofed DDoS Attacks in Software-defined Networking

Sensitivity-based hybrid approach for voltage instability alleviation using ann