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

An approach to complexity theory which offers a means of analysing algorithms in terms of their tractability. The authors consider the problem in terms of parameterized languages and taking "k-slices" of the language, thus introducing readers to new classes of algorithms which may be analysed more precisely than was the case until now. The book is as self-contained as possible and includes a great deal of background material. As a result, computer scientists, mathematicians, and graduate students interested in the design and analysis of algorithms will find much of interest.

Parameterized Complexity

The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

PART I: FOUNDATIONS 1. Introduction to Fixed-Parameter Algorithms 2. Preliminaries and Agreements 3. Parameterized Complexity Theory - A Primer 4. Vertex Cover - An Illustrative Example 5. The Art of Problem Parameterization 6. Summary and Concluding Remarks PART II: ALGORITHMIC METHODS 7. Data Reduction and Problem Kernels 8. Depth-Bounded Search Trees 9. Dynamic Programming 10. Tree Decompositions of Graphs 11. Further Advanced Techniques 12. Summary and Concluding Remarks PART III: SOME THEORY, SOME CASE STUDIES 13. Parameterized Complexity Theory 14. Connections to Approximation Algorithms 15. Selected Case Studies 16. Zukunftsmusik References Index

Invitation to fixed-parameter algorithms

In this paper, we give for constant $k$ a linear-time algorithm that, given a graph $G=(V,E)$, determines whether the treewidth of $G$ is at most $k$ and, if so, finds a tree-decomposition of $G$ with treewidth at most $k$. A consequence is that every minor-closed class of graphs that does not contain all planar graphs has a linear-time recognition algorithm. Another consequence is that a similar result holds when we look instead for path-decompositions with pathwidth at most some constant $k$.

/pdf/a-linear-time-algorithm-for-finding-tree-decompositions-of-4u8h6zpm0r.pdf

A Linear-Time Algorithm for Finding Tree-Decompositions of Small Treewidth

We describe new results in parameterized complexity theory, including an analogue of Ladner's theorem, and natural problems concerning k-move games which are complete for parameterized problem classes that are analogues of P-space.

Fixed-Parameter Intractability II (Extended Abstract)

A refined search tree technique for Dominating Set on planar graphs

We provide parameterized algorithms for NONBLOCKER, the parametric dual of the well known DOMINATING SET problem. We exemplify three methodologies for deriving parameterized algorithms that can be used in other circumstances as well, including the (i) use of extremal combinatorics (known results from graph theory) in order to obtain very small kernels, (ii) use of known exact algorithms for the (nonparameterized) MINIMUM DOMINATING SET problem, and (iii) use of exponential space. Parameterized by the size κ d of the non-blocking set, we obtain an algorithm that runs in time O*(1.4123 k d) when allowing exponential space.

NONBLOCKER : Parameterized algorithmics for MINIMUM DOMINATING SET

A fundamental problem in computational biology is the determination of the correct species tree for a set of taxa given a set of (possibly contradictory) gene trees. In recent literature, the DUPLICATION/ LOSS model has received considerable attention. Here one measures the similarity/dissimilarity between a set of gene trees by counting the number of paralogous gene duplications and subsequent gene losses which need to be postulated in order to explain (in an evolutionarily meaningful way) how the gene trees could have arisen with respect to the species tree. Here we count the number of multiple gene duplication events (duplication events in the genome of the organism involving one or more genes) without regard to gene losses. MULTIPLE GENE DUPLICATION asks to find the species tree S which requires the fewest number of multiple gene duplication events to be postulated in order to explain a set of gene trees G1, G2,..., Gk. We also examine the related problem which assumes the species tree S is known and asks to find the explanation for G1, G2,..., Gk requiring the fewest multiple gene duplications. Via a reduction to and from a combinatorial model we call the BALL AND TRAP GAME, we show that the general form of this problem is NP-hard and various parameterized versions are hard for the complexity class W[1]. These results immediately imply that MULTIPLE GENE DUPLICATION is similarily hard. We prove that several parameterized variants are in FPT.

http://www.mrfellows.net/papers/C37-multiplegene-ISAAC98.pdf

On the Multiple Gene Duplication Problem

http://www.mrfellows.net/papers/FM03_MinimizeTardyTasks.pdf

Michael R. Fellows

Papers

Fixed-Parameter Intractability II (Extended Abstract)

A refined search tree technique for Dominating Set on planar graphs

NONBLOCKER : Parameterized algorithmics for MINIMUM DOMINATING SET

On the Multiple Gene Duplication Problem

On the parametric complexity of schedules to minimize tardy tasks