CiteULike: Tag kdd

Data mining and knowledge discovery 1996 to 2005: overcoming the hype and moving from “university” to “business” and “analytics”

Gregory Piatetsky-Shapiro — 2007-08-24T13:40:11-00:00

Data Mining and Knowledge Discovery, Vol. 15, No. 1. (2007), pp. 99-105.

Abstract I survey the transformation of the data mining and knowledge discovery field over the last 10 years from the unique vantage point of KDnuggets as a leading chronicler of the field. Analysis of the most frequent words in KDnuggets News leads to revealing observations.

Background and overview for KDD Cup 2002 task 1: information extraction from biomedical articles

Alexander Yeh — 2006-02-04T09:52:35-00:00

SIGKDD Explor. Newsl., Vol. 4, No. 2. (December 2002), pp. 87-89.

A survey on wavelet applications in data mining

Tao Li — 2006-12-04T19:45:47-00:00

SIGKDD Explor. Newsl., Vol. 4, No. 2. (December 2002), pp. 49-68.

Exploiting a support-based upper bound of Pearson's correlation coefficient for efficiently identifying strongly correlated pairs

Hui Xiong — 2007-08-07T01:32:12-00:00

(2004), pp. 334-343.

Intelligent Data Analysis

Michael Berthold — 2005-10-06T09:43:41-00:00

(15 April 2003)

This monograph is a detailed introductory presentation of the key classes of intelligent data analysis methods. The twelve coherently written chapters by leading experts provide complete coverage of the core issues. The first half of the book is devoted to the discussion of classical statistical issues, ranging from the basic concepts of probability, through general notions of inference, to advanced multivariate and time series methods, as well as a detailed discussion of the increasingly important Bayesian approaches and Support Vector Machines. The following chapters then concentrate on the area of machine learning and artificial intelligence and provide introductions into the topics of rule induction methods, neural networks, fuzzy logic, and stochastic search methods. The book concludes with a chapter on Visualization and a higher-level overview of the IDA processes, which illustrates the breadth of application of the presented ideas.

Introduction to Machine Learning (Adaptive Computation and Machine Learning)

Ethem Alpaydin — 2005-08-18T18:36:42-00:00

(01 October 2004)

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, recognize faces or spoken speech, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. It discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The book can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods. After an introduction that defines machine learning and gives examples of machine learning applications, the book covers supervised learning, Bayesian decision theory, parametric methods, multivariate methods, dimensionality reduction, clustering, nonparametric methods, decision trees, linear discrimination, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, combining multiple learners, and reinforcement learning.

Data Mining.

Ian Witten — 2005-10-04T14:53:45-00:00

(01 January 2001)

Knowledge Discovery in Databases : Techniken und Anwendungen

Martin Ester — 2005-10-04T14:45:39-00:00

(27 September 2000)

Knowledge Discovery in Databases (KDD) ist ein aktuelles Forschungs- und Anwendungsgebiet der Informatik. Ziel des KDD ist es, selbständig entscheidungsrelevante, aber bisher unbekannte Zusammenhänge und Verknüpfungen in den Daten großer Datenmengen zu entdecken und dem Analysten oder dem Anwender in übersichtlicher Form zu präsentieren. Die Autoren stellen die Techniken und Anwendungen dieses interdisziplinären Gebiets anschaulich dar.

Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations

Ian Witten — 2005-04-22T17:17:56-00:00

(11 October 1999)

Data mining techniques are used to power intelligent software, both on and off the Internet. Data Mining: Practical Machine Learning Tools explains the magic behind information extraction in a book that succeeds at bringing the latest in computer science research to any IS manager or developer. In addition, this book provides an opportunity for the authors to showcase their powerful reusable Java class library for building custom data mining software. This text is remarkable with its comprehensive review of recent research on machine learning, all told in a very approachable style. (While there is plenty of math in some sections, the authors' explanations are always clear.) The book tours the nature of machine learning and how it can be used to find predictive patterns in data comprehensible to managers and developers alike. And they use sample data (for such topics as weather, contact lens prescriptions, and flowers) to illustrate key concepts. After setting out to explain the types of machine learning models (like decision trees and classification rules), the book surveys algorithms used to implement them, plus strategies for improving performance and the reliability of results. Later the book turns to the authors' downloadable Weka (rhymes with "Mecca") Java class library, which lets you experiment with data mining hands-on and gets you started with this technology in custom applications. Final sections look at the bright prospects for data mining and machine learning on the Internet (for example, in Web search engines). Precise but never pedantic, this admirably clear title delivers a real-world perspective on advantages of data mining and machine learning. Besides a programming how-to, it can be read profitably by any manager or developer who wants to see what leading-edge machine learning techniques can do for their software. --Richard Dragan Topics covered: Data mining and machine learning basics, sample datasets and applications for data mining, machine learning vs. statistics, the ethics of data mining, generalization, concepts, attributes, missing values, decision tables and trees, classification rules, association rules, exceptions, numeric prediction, clustering, algorithms and implementations in Java, inferring rules, statistical modeling, covering algorithms, linear models, support vector machines, instance-based learning, credibility, cross-validation, probability, costs (lift charts and ROC curves), selecting attributes, data cleansing, combining multiple models (bagging, boosting, and stacking), Weka (reusable Java classes for machine learning), customizing Weka, visualizing machine learning, working with massive datasets, text mining, and e-mail and the Internet.

KDD CUP-2005 report: facing a great challenge

Ying Li — 2008-02-14T11:18:34-00:00

SIGKDD Explor. Newsl., Vol. 7, No. 2. (December 2005), pp. 91-99.

Knowledge discovery in databases - an overview

WJ Frawley — 2006-08-29T16:10:47-00:00

Ai Magazine, Vol. 13 (1992), pp. 57-70.

this article. 0738-4602/92/$4.00 1992 AAAI 58 AI MAGAZINE for the 1990s (Silberschatz, Stonebraker, and Ullman 1990)

From data mining to knowledge discovery: an overview

Usama Fayyad — 2007-01-26T20:38:28-00:00

(1996), pp. 1-34.

Active exploration for learning rankings from clickthrough data

Filip Radlinski — 2007-12-25T11:40:39-00:00

(2007), pp. 570-579.

The KDD process for extracting useful knowledge from volumes of data

Usama Fayyad — 2007-11-09T01:11:45-00:00

Commun. ACM, Vol. 39, No. 11. (November 1996), pp. 27-34.

What makes patterns interesting in knowledge discovery systems

A Silberschatz — 2007-12-24T10:19:06-00:00

Transactions on Knowledge and Data Engineering, Vol. 8, No. 6. (1996), pp. 970-974.

One of the central problems in the field of knowledge discovery is the development of good measures of interestingness of discovered patterns. Such measures of interestingness are divided into objective measures-those that depend only on the structure of a pattern and the underlying data used in the discovery process, and the subjective measures-those that also depend on the class of users who examine the pattern. The focus of the paper is on studying subjective measures of interestingness. These measures are classified into actionable and unexpected, and the relationship between them is examined. The unexpected measure of interestingness is defined in terms of the belief system that the user has. Interestingness of a pattern is expressed in terms of how it affects the belief system. The paper also discusses how this unexpected measure of interestingness can be used in the discovery process

Advances in Knowledge Discovery and Data Mining

Usama Fayyad — 2007-08-09T14:42:27-00:00

(01 February 1996)

Advances in Knowledge Discovery and Data Mining brings together the latest research -- in statistics, databases, machine learning, and artificial intelligence -- that are part of the exciting and rapidly growing field of Knowledge Discovery and Data Mining. Topics covered include fundamental issues, classification and clustering, trend and deviation analysis, dependency modeling, integrated discovery systems, next generation database systems, and application case studies. The contributors include leading researchers and practitioners from academia, government laboratories, and private industry. The last decade has seen an explosive growth in the generation and collection of data. Advances in data collection, widespread use of bar codes for most commercial products, and the computerization of many business and government transactions have flooded us with data and generated an urgent need for new techniques and tools that can intelligently and automatically assist in transforming this data into useful knowledge. This book is a timely and comprehensive overview of the new generation of techniques and tools for knowledge discovery in data. Distributed for AAAI Press

Introduction to Data Mining

Pang-Ning Tan — 2006-04-21T21:37:56-00:00

(02 May 2005)

Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.

ANF: a fast and scalable tool for data mining in massive graphs

Christopher Palmer — 2006-02-09T09:53:42-00:00

(2002), pp. 81-90.

Graphical Models: Methods for Data Analysis and Mining

Christian Borgelt — 2006-01-31T18:13:15-00:00

(15 March 2002)

The use of graphical models in applied statistics has increased considerably over recent years and the theory has been greatly developed and extended. This book provides a self-contained introduction to the learning of graphical models from data, and includes detailed coverage of possibilistic networks - a tool that allows the user to infer results from problems with imprecise data. One of the most important applications of graphical modelling today is data mining - the data-driven discovery and modelling of hidden patterns in large data sets. The techniques described have a wide range of industrial applications, and a quality testing programme at a major car manufacturer is included as a real-life example. Provides a self-contained introduction to learning relational, probabilistic and possibilistic networks from data. Each concept is carefully explained and illustrated by examples. Contains all necessary background material, including modelling under uncertainty, decomposition of distributions, and graphical representation of decompositions. Features applications of learning graphical models from data, and problems for further research. Includes a comprehensive bibliography. Graphical Models: Methods for Data Analysis and Mining will be invaluable to researchers and practitioners who use graphical models in their work. Graduate students of applied statistics, computer science and engineering will find this book provides an excellent introduction to the subject.

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)

Ian Witten — 2005-10-04T14:35:45-00:00

(08 June 2005)

As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more. + Authors, Ian Witten and Eibe Frank, recipients of the 2005 ACM SIGKDD Service Award. + Algorithmic methods at the heart of successful data miningincluding tried and true techniques as well as leading edge methods; + Performance improvement techniques that work by transforming the input or output; + Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualizationin a new, interactive interface.

Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)

Jiawei Han — 2005-07-29T09:22:35-00:00

(06 September 2000)

Here's the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. Data Mining: Concepts and Techniques equips you with a sound understanding of data mining principles and teaches you proven methods for knowledge discovery in large corporate databases. Written expressly for database practitioners and professionals, this book begins with a conceptual introduction designed to get you up to speed. This is followed by a comprehensive and state-of-the-art coverage of data mining concepts and techniques. Each chapter functions as a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. Wherever possible, the authors raise and answer questions of utility, feasibility, optimization, and scalability, keeping your eye on the issues that will affect your project's results and your overall success. Data Mining: Concepts and Techniques is the master reference that practitioners and researchers have long been seeking. It is also the obvious choice for academic and professional classrooms. Classroom Features Available Online: - instructor's manual - course slides (in PowerPoint) - course supplementary readings - sample assignments and course projects * Offers a comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data. * Organized as a series of stand-alone chapters so you can begin anywhere and immediately apply what you learn. * Presents dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects. * Provides in-depth, practical coverage of essential data mining topics, including OLAP and data warehousing, data preprocessing, concept description, association rules, classification and prediction, and cluster analysis. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields.

The Elements of Statistical Learning

T Hastie — 2005-04-15T14:57:05-00:00

(09 August 2001)

During the past decade there has been an explosion in computation and information technology. With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book descibes theimprtant ideas in these areas ina common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a vluable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learing (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting--the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

Out-of-core coherent closed quasi-clique mining from large dense graph databases

Zhiping Zeng — 2007-06-21T03:53:04-00:00

ACM Trans. Database Syst., Vol. 32, No. 2. (June 2007)

Data Mining: Introductory and Advanced Topics

Margaret Dunham — 2007-06-04T10:22:17-00:00

(22 August 2002)

Graph evolution: Densification and shrinking diameters

Jure Leskovec — 2007-05-10T07:09:04-00:00

ACM Trans. Knowl. Discov. Data, Vol. 1, No. 1. (March 2007)

Correlation search in graph databases

Yiping Ke — 2007-08-21T13:19:17-00:00

(2007), pp. 390-399.

Combining Pattern Classifiers: Methods and Algorithms

Ludmila Kuncheva — 2006-09-02T17:12:18-00:00

(01 July 2004)

Covering pattern classification methods, Combining Classifiers: Ideas and Methods focuses on the important and widely studied issue of how to combine several classifiers together in order to achieve improved recognition performance. It is one of the first books to provide unified, coherent, and expansive coverage of the topic and as such will be welcomed by those involved in the area. With case studies that bring the text alive and demonstrate 'real-world' applications it is destined to become essential reading. Covering pattern classification methods, Combining Classifiers: Ideas and Methods focuses on the important and widely studied issue of how to combine several classifiers together in order to achieve improved recognition performance. It is one of the first books to provide unified, coherent, and expansive coverage of the topic and as such will be welcomed by those involved in the area. With case studies that bring the text alive and demonstrate 'real-world' applications it is destined to become essential reading.

Graph mining: Laws, generators, and algorithms

Deepayan Chakrabarti — 2006-07-21T13:00:13-00:00

ACM Comput. Surv., Vol. 38, No. 1. (2006)

Data Preparation for Data Mining (The Morgan Kaufmann Series in Data Management Systems)

Dorian Pyle — 2007-08-09T14:42:26-00:00

(15 March 1999)

Data Preparation for Data Mining addresses an issue unfortunately ignored by most authorities on data mining: data preparation. Thanks largely to its perceived difficulty, data preparation has traditionally taken a backseat to the more alluring question of how best to extract meaningful knowledge. But without adequate preparation of your data, the return on the resources invested in mining is certain to be disappointing. Dorian Pyle corrects this imbalance. A twenty-five-year veteran of what has become the data mining industry, Pyle shares his own successful data preparation methodology, offering both a conceptual overview for managers and complete technical details for IT professionals. Apply his techniques and watch your mining efforts pay off-in the form of improved performance, reduced distortion, and more valuable results. On the enclosed CD-ROM, you'll find a suite of programs as C source code and compiled into a command-line-driven toolkit. This code illustrates how the author's techniques can be applied to arrive at an automated preparation solution that works for you. Also included are demonstration versions of three commercial products that help with data preparation, along with sample data with which you can practice and experiment. * Offers in-depth coverage of an essential but largely ignored subject. * Goes far beyond theory, leading you-step by step-through the author's own data preparation techniques. * Provides practical illustrations of the author's methodology using realistic sample data sets. * Includes algorithms you can apply directly to your own project, along with instructions for understanding when automation is possible and when greater intervention is required. * Explains how to identify and correct data problems that may be present in your application. * Prepares miners, helping them head into preparation with a better understanding of data sets and their limitations.

Mining the space of graph properties

Glen Jeh — 2006-05-12T21:36:34-00:00

(2004), pp. 187-196.

Using structure indices for efficient approximation of network properties

Matthew Rattigan — 2006-11-30T23:24:24-00:00

(2006), pp. 357-366.

A fast k-means implementation using coresets

Gereon Frahling — 2007-09-07T13:53:15-00:00

(2006), pp. 135-143.

Closing the Loop: An Agenda- and Justification-Based Framework for Selecting the Next Discovery Task to Perform

Gary Livingston — 2007-10-16T18:57:04-00:00

(2001), pp. 385-392.

We propose and evaluate an agenda- and justificationbased architecture for discovery systems that selects the next tasks to perform. This framework has many desirable properties: (1) it facilitates the encoding of general discovery strategies using a variety of background knowledge, (2) it reasons about the appropriateness of the tasks being considered, and (3) it tailors its behavior toward a user's interests. A prototype discovery program called HAMB demonstrates that both reasons and...

Closing the Loop: Heuristics for Autonomous Discovery

Gary Livingston — 2007-10-16T18:56:22-00:00

(2001), pp. 393-400.

Autonomous discovery systems will be able to peruse very large databases more thoroughly than people can. In a companion paper [1], we describe a general framework for autonomous systems. We present and evaluate heuristics for use in this framework. Although these heuristics were designed for a prototype system, we believe they provide good initial solutions to problems encountered when implementing fully autonomous discovery systems. As such, these heuristics may be used as the starting point...

A Survey of Methods for Scaling Up Inductive Algorithms

Foster Provost — 2005-06-15T19:54:55-00:00

Data Mining and Knowledge Discovery, Vol. 3, No. 2. (1999), pp. 131-169.

. One of the defining challenges for the KDD research community is to enable inductive learning algorithms to mine very large databases. This paper summarizes, categorizes, and compares existing work on scaling up inductive algorithms. We concentrate on algorithms that build decision trees and rule sets, in order to provide focus and specific details; the issues and techniques generalize to other types of data mining. We begin with a discussion of important issues related to scaling up. We...

Exploiting Background Knowledge in Automated Discovery

John Aronis — 2007-10-16T18:46:41-00:00

(1996), pp. 355-358.

Prior work in automated scientific discovery has been successful in finding patterns in data, given that a reasonably small set of mostly relevant features is specified. The work described in this paper places data in the context of large bodies of background knowledge. Specifically, data items are connected to multiple databases of background knowledge represented as inheritance networks. The system has made a practical impact on botanical toxicology research, which required linking examples...

Rule-space search for knowledge-based discovery

F Provost — 2007-10-16T18:43:54-00:00

(1999)

Because the knowledge discovery process is ill-defined, iterative, and requires intense interaction, algorithm flexibility is crucial. In this paper, we present a straighforward, heuristic generate-and-test search algorithm for knowledge discovery. An analysis of the literature shows that this basic algorithm underlies many of the systems that have had practical success in data mining and knowledge discovery over the past twenty years. We argue that this search algorithm has persevered because...

Survey Of Clustering Data Mining Techniques

Pavel Berkhin — 2006-03-20T05:34:41-00:00

(2002)

Clustering is a division of data into groups of similar objects. Representing the data by fewer clusters neccessarily loses certain fine details, but achieves simplification. It models data by its clusters. Data modeling puts clustering in a historial perspective rooted in mathematics, statistics and numerical analysis. From a machine learning perspective clusters correspond to hidden patterns, the search for clusters in unsupervised learning and the resulting system represents a data concept....

An interactive system for finding complementary literatures: a stimulus to scientific discovery

Don Swanson — 2008-02-29T00:09:08-00:00

Artif. Intell., Vol. 91, No. 2. (April 1997), pp. 183-203.

Information discovery from complementary literatures: categorizing viruses as potential weapons

Don Swanson — 2007-04-13T19:03:27-00:00

J. Am. Soc. Inf. Sci. Technol., Vol. 52, No. 10. (August 2001), pp. 797-812.

Algebraic decision trees and Euler characteristics

Andrew Yao — 2005-09-28T15:25:48-00:00

Theor. Comput. Sci., Vol. 141, No. 1-2. (1995), pp. 133-150.

For any set Image, let χ(S) denote its Euler characteristic. In this paper, we show that any algebraic computation tree or fixed-degree algebraic decision tree must have height Ω(log¦χ(S)¦ − cn) for deciding the membership question of a compact semi-algebraic set S. This extends a result in Björner et al. (1992), where it was shown that any linear decision tree for deciding the membership question of a closed polyhedron S must have height greater than or equal to log3¦χ(S)¦.

An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization

Thomas Dietterich — 2008-06-26T09:40:27-00:00

Machine Learning, Vol. 40, No. 2. (2000), pp. 139-157.

Bagging and boosting are methods that generate a diverse ensemble of classifiers by manipulating the training data given to a “base” learning algorithm. Breiman has pointed out that they rely for their effectiveness on the instability of the base learning algorithm. An alternative approach to generating an ensemble is to randomize the internal decisions made by the base algorithm. This general approach has been studied previously by Ali and Pazzani and by Dietterich and Kong. This paper compares the effectiveness of randomization, bagging, and boosting for improving the performance of the decision-tree algorithm C4.5. The experiments show that in situations with little or no classification noise, randomization is competitive with (and perhaps slightly superior to) bagging but not as accurate as boosting. In situations with substantial classification noise, bagging is much better than boosting, and sometimes better than randomization.

Boosting Support Vector Machines for Imbalanced Data Sets

Benjamin Wang — 2008-06-30T16:54:01-00:00

Foundations of Intelligent Systems (2008), pp. 38-47.

Real world data mining applications must address the issue of learning from imbalanced data sets. The problem occurs when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed vector spaces or lack of information. Common approaches for dealing with the class imbalance problem involve modifying the data distribution or modifying the classifier. In this work, we choose to use a combination of both approaches. We use support vector machines with soft margins as the base classifier to solve the skewed vector spaces problem. Then we use a boosting algorithm to get an ensemble classifier that has lower error than a single classifier. We found that this ensemble of SVMs makes an impressive improvement in prediction performance, not only for the majority class, but also for the minority class.

Optimization and scale-freeness for complex networks

Petter Minnhagen — 2007-11-10T15:55:38-00:00

Chaos: An Interdisciplinary Journal of Nonlinear Science, Vol. 17, No. 2. (2007)

Complex networks are mapped to a model of boxes and balls where the balls are distinguishable. It is shown that the scale-free size distribution of boxes maximizes the information associated with the boxes provided configurations including boxes containing a finite fraction of the total amount of balls are excluded. It is conjectured that for a connected network with only links between different nodes, the nodes with a finite fraction of links are effectively suppressed. It is hence suggested that for such networks the scale-free node-size distribution maximizes the information encoded on the nodes. The noise associated with the size distributions is also obtained from a maximum entropy principle. Finally, explicit predictions from our least bias approach are found to be borne out by metabolic networks.

A theory of the learnable

LG Valiant — 2006-01-16T21:03:03-00:00

Commun. ACM, Vol. 27, No. 11. (November 1984), pp. 1134-1142.

Improved Boosting Algorithms Using Confidence-rated Predictions

Robert Schapire — 2008-01-21T17:16:45-00:00

Mach. Learn., Vol. 37, No. 3. (December 1999), pp. 297-336.

Symbolic dynamic analysis of complex systems for anomaly detection

Asok Ray — 2007-12-27T17:23:56-00:00

Signal Process., Vol. 84, No. 7. (July 2004), pp. 1115-1130.

Pattern identification in dynamical systems via symbolic time series analysis

Venkatesh Rajagopalan — 2007-12-27T17:23:53-00:00

Pattern Recogn., Vol. 40, No. 11. (November 2007), pp. 2897-2907.

A Tutorial on Support Vector Machines for Pattern Recognition

Christopher Burges — 2006-02-06T08:07:22-00:00

Data Min. Knowl. Discov., Vol. 2, No. 2. (June 1998), pp. 121-167.

Symbolic time series analysis for anomaly detection: a comparative evaluation

Shin Chin — 2007-03-12T13:21:05-00:00

Signal Process., Vol. 85, No. 9. (September 2005), pp. 1859-1868.