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

Data Mining - Concepts and Techniques.

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

http://cfile28.uf.tistory.com/attach/23159E3D57117DF7107077

Twitter mood predicts the stock market.

Preface 1.1 What is Computational Intelligence? 1.2 Agents in the World 1.3 Representation and Reasoning 1.4 Applications 1.5 Overview 1.6 References and Further Reading 1.7 Exercises 2.1 Introduction 2.2 Representation and Reasoning Systems 2.3 Simplifying assumptions of the initial RRS 2.4 Datalog 2.5 Semantics 2.6 Questions and Answers 2.7 Proofs 2.8 Extending the Language with Functional Symbols 2.9 References and Further Reading 2.10 Exercises 3.1 Introduction 3.2 Case Study: House Wiring 3.3 Discussion 3.5 Case-Study: Repesenting Abstract Concepts 3.6 Applications in Natural Language Processing 3.7 References and Further Reading 3.8 Exercises 4.1 Why Search? 4.2 Graph Searching 4.3 A Generic Searching Algorithm 4.4 Blind Search Strategies 4.5 Heuristic Search 4.6 Refinements to Search Strategies 4.7 Constraint Satisfaction Problems 4.8 References and Further Reading 4.9 Exercises 5.1 Introduction 5.2 Defining a solution 5.3 Choosing a Representation Language 5.4 Mapping a problem to representation 5.5 Choosing an inference procedure 5.6 References and Further Reading 5.7 Exercises 6.1 Introduction 6.2 Knowledge-Based System Architecture 6.3 Meta-Interpreters 6.4 Querying the User 6.5 Explanation 6.6 Debugging Knowledge Bases 6.7 A Meta-Interpreter with Search 6.8 Unification 6.9 References and Further Reading 6.10 Exercises 7.1 Equality 7.2 Integrity Constraints 7.3 Complete Knowledge Assumption 7.4 Disjunctive Knowledge 7.5 Explicit Quantification 7.6 First-order predicate calculus 7.7 Modal Logic 7.8 References and Further Reading 7.9 Exercises 8.1 Introduction 8.2 Representations of Actions and Change 8.3 Reasoning with World Representations 8.4 References and Further Reading 8.5 Exercises 9.1 Introduction 9.2 An Assumption-Based Reasoning Framework 9.3 Default Reasoning 9.4 Abduction 9.5 Evidential and Causal Reasoning 9.6 Algorithms for Assumption-based Reasoning 9.7 References and Further Reading 9.8 Exercises 10.1 Introduction 10.2 Probability 10.3 Independence Assumptions 10.4 Making Decisions Under Uncertainty 10.5 References and Further Reading 10.6 Exercises 11.1 Introduction 11.2 Learning as choosing the best representation 11.3 Case-based reasoning 11.4 Learning as refining the hypothesis space 11.5 Learning Under Uncertainty 11.6 Explanation-based Learning 11.7 References and Further Reading 11.8 Exercises 12.1 Introduction 12.2 Robotic Systems 12.3 The Agent function 12.4 Designing Robots 12.5 Uses of Agent models 12.6 Robot Architectures 12.7 Implementing a Controller 12.8 Robots Modelling the World 12.9 Reasoning in Situated Robots 12.10 References and Further Reading 12.11 Exercises Appendices A Glossary B The Prolog Programming Language B.1 Introduction B.2 Interacting with Prolog B.3 Syntax B.5 Database Relations B.6 Returning All Answers B.7 Input and Output B.8 Controlling Search C.Some more Implemented Systems C.1 Bottom-Up Interpreters C.2 Top-down Interpreters C.3 A Constraint Satisfaction Problem Solver C.4 Neural Network Learner C.5 Partial-Order Planner C.6 Implementing Belief Networks C.7 Robot Controller

Computational Intelligence: A Logical Approach

We provide an introduction to Theorist, a logic programming system that uses a uniform deductive reasoning mechanism to construct explanations of observations in terms of facts and hypotheses. Observations, facts, and possible hypotheses are each sets of logical formulas that represent, respectively, a set of observations on a partial domain, a set of facts for which the domain is a model, and a set of tentative hypotheses which may be required to provide a consistent explanation of the observations.

Theorist: A Logical Reasoning System for Defaults and Diagnosis

Explainable AI is not a new field. Since at least the early exploitation of C.S. Pierce’s abductive reasoning in expert systems of the 1980s, there were reasoning architectures to support an explanation function for complex AI systems, including applications in medical diagnosis, complex multi-component design, and reasoning about the real world. So explainability is at least as old as early AI, and a natural consequence of the design of AI systems. While early expert systems consisted of handcrafted knowledge bases that enabled reasoning over narrowly well-defined domains (e.g., INTERNIST, MYCIN), such systems had no learning capabilities and had only primitive uncertainty handling. But the evolution of formal reasoning architectures to incorporate principled probabilistic reasoning helped address the capture and use of uncertain knowledge.

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Explainable AI: The new 42?

There has been much recent research on identifying global community structure in networks. However, most existing approaches require complete information of the graph in question, which is impractical for some networks, e.g. the World Wide Web (WWW). Algorithms for local community detection have been proposed but their results usually contain many outliers. In this paper, we propose a new measure of local community structure, coupled with a two-phase algorithm that extracts all possible candidates first, and then optimizes the community hierarchy. We compare our results with previous methods on real world networks such as the co-purchase network from Amazon. Experimental results verify the feasibility and effectiveness of our approach.

/pdf/local-community-identification-in-social-networks-540pngpx6u.pdf

Local Community Identification in Social Networks

The KDD 2002 conference, held from 23rd to 26th July 2002, was the eighth in the series. It represented a return to the country in which the series was launched: the first was held in Montreal, Canada, and this, the eighth, was held in Edmonton, Canada. In the years between the first conference in the series and this present one, data mining has be, come a well-established discipline. It has continued to strengthen its links to other data analytic disciplines, including statistics, machine learning, pattern recognition, visualization, and database technology, but has now clearly carved out a niche of its own. Over the period in which this series has been running, hardware technology has continued to advance in great leaps, with the result that large databases have continued to grow in both number and size. The implication is that the challenge of data mining is even more important, that the problems requiring data mining solutions are ever more ubiquitous, and that new tools and methods for tackling are even more necessary.KDD 2002 received a record number of submitted papers - 307 in total, 37 of which were considered for the industral/applicafion track. Among the 270 research submissions, 32 were selected (12%) for full papers; and among the 37 industrial/application submissions, 12 (32%) were selected for full papers. An additional 44 submissions were chosen to be presented as posters, a vast majority of which were research submissions. This low rate of acceptance reflects a conscious effort to maintain the very high standards of quality and relevance, which have been achieved by previous conferences in the series. It means that the papers and posters in the proceedings represent the cutting edge of data mining problemsl solutions, and technology. On the other hand, this policy inevitably meant that many excellent contributions did not make it to the final program. The choice had to be informed by balance as well as quality - KDD 2002 had to showcase research in data mining across the entire frontier of the discipline. This breadth was reflected in the choice of invited speakers, both well known in the data mining; community, but from different backgrounds: Daryl Pregibon and Geoff Hinton. The program also includes 6 workshops in such diverse areas as 'Data Mining in Bioinformatics', 'Web Mining', 'Multimedia Data Mining', 'Multi-Relational Data Mining', 'Temporal Data Mining', and 'Fractals in Data Mining' as well as 6 tutorials on 'Text Mining for Bioinformatics', 'Querying and Mining Data Streams', 'Link Analysis', 'Multivariate Density Estimation', 'Common Reasons Data Mining Projects Fail', and 'Visual Data Mining'.

Randy Goebel

Papers

Computational Intelligence: A Logical Approach

Theorist: A Logical Reasoning System for Defaults and Diagnosis

Explainable AI: The new 42?

Local Community Identification in Social Networks

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining