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MARC Lista (tabellare)
Analyzing the large numbers of variables in biomedical and satellite imagery / / Phillip I. Good
Analyzing the large numbers of variables in biomedical and satellite imagery / / Phillip I. Good
Autore Good Phillip I
Edizione [1st ed.]
Pubbl/distr/stampa Hoboken, N.J., : Wiley, c2011
Descrizione fisica xii, 185 p. : ill
Disciplina 006.3/12
Soggetto topico Data mining
Mathematical statistics
Biomedical engineering - Data processing
Remote sensing - Data processing
Functions of several complex variables
R (Computer program language)
ISBN 1-283-13877-8
0-470-93725-4
9786613138774
0-470-93727-0
1-118-00214-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto ; Machine generated contents note: ; 1. Very Large Arrays -- ; 1.1. Applications -- ; 1.2. Problems -- ; 1.3. Solutions -- ; 2. Permutation Tests -- ; 2.1. Two-Sample Comparison -- ; 2.1.1. Blocks -- ; 2.2. k-Sample Comparison -- ; 2.3. Computing The p-Value -- ; 2.3.1. Monte Carlo Method -- ; 2.3.2. An R Program -- ; 2.4. Multiple-Variable Comparisons -- ; 2.4.1. Euclidean Distance Matrix Analysis -- ; 2.4.2. Hotelling's T2 -- ; 2.4.3. Mantel's U -- ; 2.4.4. Combining Univariate Tests -- ; 2.4.5. Gene Set Enrichment Analysis -- ; 2.5. Categorical Data -- ; 2.6. Software -- ; 2.7. Summary -- ; 3. Applying the Permutation Test -- ; 3.1. Which Variables Should Be Included? -- ; 3.2. Single-Value Test Statistics -- ; 3.2.1. Categorical Data -- ; 3.2.2. A Multivariate Comparison Based on a Summary Statistic -- ; 3.2.3. A Multivariate Comparison Based on Variants of Hotelling's T2
; 3.2.4. Adjusting for Covariates -- ; 3.2.5. Pre-Post Comparisons -- ; 3.2.6. Choosing a Statistic: Time-Course Microarrays -- ; 3.3. Recommended Approaches -- ; 3.4. To Learn More -- ; 4. Biological Background -- ; 4.1. Medical Imaging -- ; 4.1.1. Ultrasound -- ; 4.1.2. EEG/MEG -- ; 4.1.3. Magnetic Resonance Imaging -- ; 4.1.3.1. MRI -- ; 4.1.3.2. fMRI -- ; 4.1.4. Positron Emission Tomography -- ; 4.2. Microarrays -- ; 4.3. To Learn More -- ; 5. Multiple Tests -- ; 5.1. Reducing the Number of Hypotheses to Be Tested -- ; 5.1.1. Normalization -- ; 5.1.2. Selection Methods -- ; 5.1.2.1. Univariate Statistics -- ; 5.1.2.2. Which Statistic? -- ; 5.1.2.3. Heuristic Methods -- ; 5.1.2.4. Which Method? -- ; 5.2. Controlling the Over All Error Rate -- ; 5.2.1. An Example: Analyzing Data from Microarrays -- ; 5.3. Controlling the False Discovery Rate -- ; 5.3.1. An Example: Analyzing Time-Course Data from Microarrays -- ; 5.4. Gene Set Enrichment Analysis
; 5.5. Software for Performing Multiple Simultaneous Tests -- ; 5.5.1. AFNI -- ; 5.5.2. Cyber-T -- ; 5.5.3. dChip -- ; 5.5.4. ExactFDR -- ; 5.5.5. GESS -- ; 5.5.6. HaploView -- ; 5.5.7. MatLab -- ; 5.5.8. R -- ; 5.5.9. SAM -- ; 5.5.10. ParaSam -- ; 5.6. Summary -- ; 5.7. To Learn More -- ; 6. The Bootstrap -- ; 6.1. Samples and Populations -- ; 6.2. Precision of an Estimate -- ; 6.2.1. R Code -- ; 6.2.2. Applying the Bootstrap -- ; 6.2.3. Bootstrap Reproducibility Index -- ; 6.2.4. Estimation in Regression Models -- ; 6.3. Confidence Intervals -- ; 6.3.1. Testing for Equivalence -- ; 6.3.2. Parametric Bootstrap -- ; 6.3.3. Blocked Bootstrap -- ; 6.3.4. Balanced Bootstrap -- ; 6.3.5. Adjusted Bootstrap -- ; 6.3.6. Which Test? -- ; 6.4. Determining Sample Size -- ; 6.4.1. Establish a Threshold -- ; 6.5. Validation -- ; 6.5.1. Cluster Analysis -- ; 6.5.2. Correspondence Analysis -- ; 6.6. Building a Model -- ; 6.7. How Large Should The Samples Be?
; 6.8. Summary -- ; 6.9. To Learn More -- ; 7. Classification Methods -- ; 7.1. Nearest Neighbor Methods -- ; 7.2. Discriminant Analysis -- ; 7.3. Logistic Regression -- ; 7.4. Principal Components -- ; 7.5. Naive Bayes Classifier -- ; 7.6. Heuristic Methods -- ; 7.7. Decision Trees -- ; 7.7.1. A Worked-Through Example -- ; 7.8. Which Algorithm Is Best for Your Application? -- ; 7.8.1. Some Further Comparisons -- ; 7.8.2. Validation Versus Cross-validation -- ; 7.9. Improving Diagnostic Effectiveness -- ; 7.9.1. Boosting -- ; 7.9.2. Ensemble Methods -- ; 7.9.3. Random Forests -- ; 7.10. Software for Decision Trees -- ; 7.11. Summary -- ; 8. Applying Decision Trees -- ; 8.1. Photographs -- ; 8.2. Ultrasound -- ; 8.3. MRI Images -- ; 8.4. EEGs and EMGs -- ; 8.5. Misclassification Costs -- ; 8.6. Receiver Operating Characteristic -- ; 8.7. When the Categories Are As Yet Undefined -- ; 8.7.1. Unsupervised Principal Components Applied to fMRI
; 8.7.2. Supervised Principal Components Applied to Microarrays -- ; 8.8. Ensemble Methods -- ; 8.9. Maximally Diversified Multiple Trees -- ; 8.10. Putting It All Together -- ; 8.11. Summary -- ; 8.12. To Learn More -- Glossary of Biomedical Terminology -- Glossary of Statistical Terminology -- Appendix: An R Primer -- ; R1. Getting Started -- ; R1.1. R Functions -- ; R1.2. Vector Arithmetic -- ; R2. Store and Retrieve Data -- ; R2.1. Storing and Retrieving Files from Within R -- ; R2.2. The Tabular Format -- ; R2.3. Comma Separated Format -- ; R3. Resampling -- ; R3.1. The While Command -- ; R4. Expanding R's Capabilities -- ; R4.1. Downloading Libraries of R Functions -- ; R4.2. Programming Your Own Functions.
Record Nr. UNINA-9910825098303321
Good Phillip I  
Hoboken, N.J., : Wiley, c2011
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Automated data collection with R : a practical guide to web scraping and text mining / / Simon Munzert [and three others]
Automated data collection with R : a practical guide to web scraping and text mining / / Simon Munzert [and three others]
Autore Munzert Simon
Pubbl/distr/stampa Chichester, England : , : Wiley, , 2015
Descrizione fisica 1 online resource (XXII, 453 p.)
Disciplina 006.3/12
Soggetto topico Data mining
Automatic data collection systems
Social sciences - Research - Data processing
R (Computer program language)
ISBN 1-118-83480-1
1-118-83473-9
1-118-83478-X
Classificazione COM021030
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Machine generated contents note: Dedication Table of Contents List of Figures List of Tables Preface 1 Introduction 1.1 Case Study: World Heritage Sites in Danger 1.2 Some Remarks on Web Data Quality 1.3 Technologies for Disseminating, Extracting and Storing Web Data 1.3.1 Technologies for disseminating content on the Web 1.4 Structure of the Book Part One A Primer on Web and Data Technologies 2 HTML 2.1 Browser Presentation and Source Code 2.2 Syntax Rules 2.3 Tags and Attributes 2.4 Parsing Summary Further Reading Problems 3 XML and JSON 3.1 A Short Example XML Document 3.2 XML Syntax Rules 3.3 When Is an XML Document Well-formed or Valid? 3.4 XML Extensions and Technologies 3.5 XML and R in Practice 3.6 A Short Example JSON Document 3.7 JSON Syntax Rules 3.8 JSON and R in Practice Summary Further Reading Problems 4 XPath 4.1 XPath - a Querying Language for Web Documents 4.2 Identifying Node Sets with XPath 4.3 Extracting Node Elements Summary Further Reading Problems 5 HTTP 5.1 HTTP Fundamentals 5.2 Advanced Features of HTTP 5.3 Protocols beyond HTTP 5.4 HTTP in Action Summary Further Reading Problems 6 AJAX 6.1 JavaScript 6.2 XHR 6.3 Exploring AJAX with Web Developer Tools Summary Further Reading Problems 7 SQL and Relational Databases 7.1 Overview and Terminology 7.2 Relational Databases 7.3 SQL: a Language to Communicate with Databases 7.4 Databases in Action Summary Further Reading Problems 8 Regular Expressions and String Functions 8.1 Regular Expressions 8.2 String Processing 8.3 A Word on Character Encodings Summary Further Reading Problems Part Two A Practical Toolbox for Web Scraping and Text Mining 9 Scraping the Web 9.1 Retrieval Scenarios 9.2 Extraction Strategies 9.3 Web Scraping: Good Practice 9.4 Valuable Sources of Inspiration Summary Further Reading Problems 10 Statistical Text Processing 10.1 The running example: classifying press releases of the British government 10.2 Processing Textual Data 10.3 Supervised Learning Techniques 10.4 Unsupervised Learning Techniques Summary Further reading 11 Managing Data Projects 11.1 Interacting with the File System 11.2 Processing Multiple Documents/Links 11.3 Organizing Scraping Procedures 11.4 Executing R Scripts on a Regular Basis Part Three A Bag of Case Studies 12 Collaboration Networks in the U.S. Senate 12.1 Information on the Bills 12.2 Information on the Senators 12.3 Analyzing the network structure 12.4 Conclusion 13 Parsing Information from Semi-Structured Documents 13.1 Downloding Data from the FTP Server 13.2 Parsing Semi-Structured Text Data 13.3 Visualizing station and temperature data 14 Predicting the 2014 Academy Awards using Twitter 14.1 Twitter APIs: Overview 14.2 Twitter-based Forecast of the 2014 Academy Awards 14.3 Conclusion 15 Mapping the Geographic Distribution of Names 15.1 Developing a Data Collection Strategy 15.2 Web Site Inspection 15.3 Data Retrieval and Information Extraction 15.4 Mapping Names 15.5 Automating the Process 15.6 Summary 16 Gathering Data on Mobile Phones 16.1 Page Exploration 16.2 Scraping Procedure 16.3 Graphical Analysis 16.4 Data storage 17 Analyzing Sentiments of Product Reviews 17.1 Introduction 17.2 Collecting the data 17.3 Analyzing the Data 17.4 Conclusion References Bibliography Indices General Index Package Index Function Index .
Record Nr. UNINA-9910132342003321
Munzert Simon  
Chichester, England : , : Wiley, , 2015
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Automated data collection with R : a practical guide to web scraping and text mining / / Simon Munzert [and three others]
Automated data collection with R : a practical guide to web scraping and text mining / / Simon Munzert [and three others]
Autore Munzert Simon
Pubbl/distr/stampa Chichester, England : , : Wiley, , 2015
Descrizione fisica 1 online resource (XXII, 453 p.)
Disciplina 006.3/12
Soggetto topico Data mining
Automatic data collection systems
Social sciences - Research - Data processing
R (Computer program language)
ISBN 1-118-83480-1
1-118-83473-9
1-118-83478-X
Classificazione COM021030
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Machine generated contents note: Dedication Table of Contents List of Figures List of Tables Preface 1 Introduction 1.1 Case Study: World Heritage Sites in Danger 1.2 Some Remarks on Web Data Quality 1.3 Technologies for Disseminating, Extracting and Storing Web Data 1.3.1 Technologies for disseminating content on the Web 1.4 Structure of the Book Part One A Primer on Web and Data Technologies 2 HTML 2.1 Browser Presentation and Source Code 2.2 Syntax Rules 2.3 Tags and Attributes 2.4 Parsing Summary Further Reading Problems 3 XML and JSON 3.1 A Short Example XML Document 3.2 XML Syntax Rules 3.3 When Is an XML Document Well-formed or Valid? 3.4 XML Extensions and Technologies 3.5 XML and R in Practice 3.6 A Short Example JSON Document 3.7 JSON Syntax Rules 3.8 JSON and R in Practice Summary Further Reading Problems 4 XPath 4.1 XPath - a Querying Language for Web Documents 4.2 Identifying Node Sets with XPath 4.3 Extracting Node Elements Summary Further Reading Problems 5 HTTP 5.1 HTTP Fundamentals 5.2 Advanced Features of HTTP 5.3 Protocols beyond HTTP 5.4 HTTP in Action Summary Further Reading Problems 6 AJAX 6.1 JavaScript 6.2 XHR 6.3 Exploring AJAX with Web Developer Tools Summary Further Reading Problems 7 SQL and Relational Databases 7.1 Overview and Terminology 7.2 Relational Databases 7.3 SQL: a Language to Communicate with Databases 7.4 Databases in Action Summary Further Reading Problems 8 Regular Expressions and String Functions 8.1 Regular Expressions 8.2 String Processing 8.3 A Word on Character Encodings Summary Further Reading Problems Part Two A Practical Toolbox for Web Scraping and Text Mining 9 Scraping the Web 9.1 Retrieval Scenarios 9.2 Extraction Strategies 9.3 Web Scraping: Good Practice 9.4 Valuable Sources of Inspiration Summary Further Reading Problems 10 Statistical Text Processing 10.1 The running example: classifying press releases of the British government 10.2 Processing Textual Data 10.3 Supervised Learning Techniques 10.4 Unsupervised Learning Techniques Summary Further reading 11 Managing Data Projects 11.1 Interacting with the File System 11.2 Processing Multiple Documents/Links 11.3 Organizing Scraping Procedures 11.4 Executing R Scripts on a Regular Basis Part Three A Bag of Case Studies 12 Collaboration Networks in the U.S. Senate 12.1 Information on the Bills 12.2 Information on the Senators 12.3 Analyzing the network structure 12.4 Conclusion 13 Parsing Information from Semi-Structured Documents 13.1 Downloding Data from the FTP Server 13.2 Parsing Semi-Structured Text Data 13.3 Visualizing station and temperature data 14 Predicting the 2014 Academy Awards using Twitter 14.1 Twitter APIs: Overview 14.2 Twitter-based Forecast of the 2014 Academy Awards 14.3 Conclusion 15 Mapping the Geographic Distribution of Names 15.1 Developing a Data Collection Strategy 15.2 Web Site Inspection 15.3 Data Retrieval and Information Extraction 15.4 Mapping Names 15.5 Automating the Process 15.6 Summary 16 Gathering Data on Mobile Phones 16.1 Page Exploration 16.2 Scraping Procedure 16.3 Graphical Analysis 16.4 Data storage 17 Analyzing Sentiments of Product Reviews 17.1 Introduction 17.2 Collecting the data 17.3 Analyzing the Data 17.4 Conclusion References Bibliography Indices General Index Package Index Function Index .
Record Nr. UNINA-9910823262603321
Munzert Simon  
Chichester, England : , : Wiley, , 2015
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Big Data Analytics [[electronic resource] ] : First International Conference, BDA 2012, New Delhi, India, December 24-26, 2012, Proceedings / / edited by Srinath Srinivasa, Vasudha Bhatnagar
Big Data Analytics [[electronic resource] ] : First International Conference, BDA 2012, New Delhi, India, December 24-26, 2012, Proceedings / / edited by Srinath Srinivasa, Vasudha Bhatnagar
Edizione [1st ed. 2012.]
Pubbl/distr/stampa Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2012
Descrizione fisica 1 online resource (XIV, 181 p. 83 illus.)
Disciplina 006.3/12
Collana Information Systems and Applications, incl. Internet/Web, and HCI
Soggetto topico Application software
Artificial intelligence
Database management
Information storage and retrieval
Data mining
Algorithms
Information Systems Applications (incl. Internet)
Artificial Intelligence
Database Management
Information Storage and Retrieval
Data Mining and Knowledge Discovery
Algorithm Analysis and Problem Complexity
ISBN 3-642-35542-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Perspectives on Big Data Analytics -- Scalable Analytics – Algorithms and Systems -- Big-Data – Theoretical, Engineering and Analytics Perspective -- Data Analytics Applications -- A Comparison of Statistical Machine Learning Methods in Heartbeat Detection and Classification -- Enhanced Query-By-Object Approach for Information Requirement Elicitation in Large Databases -- Cloud Computing and Big Data Analytics: What Is New from Databases Perspective? -- A Model of Virtual Crop Labs as a Cloud Computing Application for Enhancing Practical Agricultural Education -- Knowledge Discovery through Information Extraction -- Exploiting Schema and Documentation for Summarizing Relational Databases -- Faceted Browsing over Social Media -- Analog Textual Entailment and Spectral Clustering (ATESC) Based Summarization -- Economics of Gold Price Movement-Forecasting Analysis Using Macro-economic, Investor Fear and Investor Behavior Features -- Data Models in Analytics -- An Efficient Method of Building an Ensemble of Classifiers in Streaming Data -- I/O Efficient Algorithms for Block Hessenberg Reduction Using Panel Approach -- Luring Conditions and Their Proof of Necessity through Mathematical Modelling -- Efficient Recommendation for Smart TV Contents -- Materialized View Selection Using Simulated Annealing.
Record Nr. UNISA-996465939903316
Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2012
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Bisociative Knowledge Discovery [[electronic resource] ] : An Introduction to Concept, Algorithms, Tools, and Applications / / edited by Michael R. Berthold
Bisociative Knowledge Discovery [[electronic resource] ] : An Introduction to Concept, Algorithms, Tools, and Applications / / edited by Michael R. Berthold
Autore Berthold Michael R
Edizione [1st ed. 2012.]
Pubbl/distr/stampa Cham, : Springer Nature, 2012
Descrizione fisica 1 online resource (IX, 485 p. 146 illus.)
Disciplina 006.3/12
Collana Lecture Notes in Artificial Intelligence
Soggetto topico Artificial intelligence
Data mining
Application software
User interfaces (Computer systems)
Pattern recognition
Computer communication systems
Artificial Intelligence
Data Mining and Knowledge Discovery
Information Systems Applications (incl. Internet)
User Interfaces and Human Computer Interaction
Pattern Recognition
Computer Communication Networks
Soggetto non controllato Artificial Intelligence (incl. Robotics)
Data Mining and Knowledge Discovery
Information Systems Applications (incl. Internet)
User Interfaces and Human Computer Interaction
Pattern Recognition
Computer Communication Networks
ISBN 3-642-31830-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910293146803321
Berthold Michael R  
Cham, : Springer Nature, 2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Bisociative Knowledge Discovery [[electronic resource] ] : An Introduction to Concept, Algorithms, Tools, and Applications / / edited by Michael R. Berthold
Bisociative Knowledge Discovery [[electronic resource] ] : An Introduction to Concept, Algorithms, Tools, and Applications / / edited by Michael R. Berthold
Autore Berthold Michael R
Edizione [1st ed. 2012.]
Pubbl/distr/stampa Cham, : Springer Nature, 2012
Descrizione fisica 1 online resource (IX, 485 p. 146 illus.)
Disciplina 006.3/12
Collana Lecture Notes in Artificial Intelligence
Soggetto topico Artificial intelligence
Data mining
Application software
User interfaces (Computer systems)
Pattern recognition
Computer communication systems
Artificial Intelligence
Data Mining and Knowledge Discovery
Information Systems Applications (incl. Internet)
User Interfaces and Human Computer Interaction
Pattern Recognition
Computer Communication Networks
Soggetto non controllato Artificial Intelligence (incl. Robotics)
Data Mining and Knowledge Discovery
Information Systems Applications (incl. Internet)
User Interfaces and Human Computer Interaction
Pattern Recognition
Computer Communication Networks
ISBN 3-642-31830-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996206991903316
Berthold Michael R  
Cham, : Springer Nature, 2012
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Computational Analysis of Terrorist Groups: Lashkar-e-Taiba / / by V.S. Subrahmanian, Aaron Mannes, Amy Sliva, Jana Shakarian, John P. Dickerson
Computational Analysis of Terrorist Groups: Lashkar-e-Taiba / / by V.S. Subrahmanian, Aaron Mannes, Amy Sliva, Jana Shakarian, John P. Dickerson
Autore Subrahmanian V.S
Edizione [1st ed. 2013.]
Pubbl/distr/stampa New York, NY : , : Springer New York : , : Imprint : Springer, , 2013
Descrizione fisica 1 online resource (232 p.)
Disciplina 006.3/12
519.5
Soggetto topico Computers
Artificial intelligence
Data mining
Application software
Mathematical logic
Information Systems and Communication Service
Artificial Intelligence
Data Mining and Knowledge Discovery
Computer Appl. in Social and Behavioral Sciences
Mathematical Logic and Formal Languages
ISBN 1-283-62446-X
9786613936912
1-4614-4769-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction -- A Brief History of LeT -- Temporal Probabilistic Behavior Rules -- Targeting Civilians -- Attacks Against Public Sites, Tourist Sites, and Transportation Facilities -- Attacks Against Professional Security Forces -- Attacks Against Security Installations and Infrastructure -- Other Types of Attacks -- Armed Clashes -- Computing Policy Options -- Policy Options Against LeT.
Record Nr. UNINA-9910437601803321
Subrahmanian V.S  
New York, NY : , : Springer New York : , : Imprint : Springer, , 2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Computational framework for knowledge [[electronic resource] ] : integrated behavior of machines / / Syed V. Ahamed
Computational framework for knowledge [[electronic resource] ] : integrated behavior of machines / / Syed V. Ahamed
Autore Ahamed Syed V. <1938->
Pubbl/distr/stampa Hoboken, N.J., : John Wiley & Sons, c2009
Descrizione fisica 1 online resource (568 p.)
Disciplina 006.3
006.3/12
Soggetto topico Data mining
Web databases
Knowledge acquisition (Expert systems)
Soggetto genere / forma Electronic books.
ISBN 1-282-27896-7
9786612278969
0-470-48042-4
0-470-48041-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto COMPUTATIONAL FRAMEWORK FOR KNOWLEDGE; CONTENTS; Foreword; Preface; Introduction; 1 New Knowledge Environments; Chapter Summary; 1.1 The Need to Know; 1.1.1 Global Power of Knowledge; 1.1.2 Scientific Aspects; 1.1.3 Wealth Aspects; 1.2 Role of Technology; 1.2.1 Three Major Contributions; 1.2.2 A String of Secondary Contributions; 1.2.3 Peripheral Contributions; 1.3 Knowledge and Wealth; 1.4 Evolving Knowledge Environments; 1.4.1 Components of Knowledge; 1.4.2 The Processing of Knowledge; 1.5 Structure and Communication of Knowledge; 1.5.1 Velocity of Flow of Knowledge
1.5.2 Truisms in the Knowledge Domain1.5.3 Philosophic Validation of Knowledge; 1.5.4 Scientific Principles in the Knowledge Domain; 1.5.5 Aspects of Knowledge; 1.6 Intelligent Internet and Knowledge Society; 1.6.1 Four Precursors of Modern Wisdom; 1.6.2 Knowledge Bases to Derive Wisdom; 1.6.3 Role of National Governments; 1.6.4 Universal Knowledge-Processing Systems; 1.6.5 Educational Networks; 1.6.6 Medical Networks; 1.6.7 Antiterrorism Networks; 1.7 Knowledge Networks; 1.7.1 Evolution of Knowledge Networks; 1.7.2 Knowledge Network Configuration; 1.8 Conclusions; References
2 Wisdom MachinesChapter Summary; 2.1 Many "Flavors" of Wisdom; 2.2 Three Orientations of Wisdom; 2.2.1 Absolute Wisdom; 2.2.2 Materialistic Wisdom; 2.2.3 Opportunistic Wisdom; 2.2.4 Needs and Wisdom; 2.2.5 What Are Wisdom Machines?; 2.3 Optimization of Wise Choices; 2.3.1 Derived Axioms; 2.3.2 Priming of Machine Wisdom for Directional Axioms; 2.4 Three-Level Functions; 2.4.1 Level I: Access and Administrative Functions; 2.4.2 Level II: Linkage, Scientific, and Statistical Functions; 2.4.3 Level III: Human Authentication; 2.5 Knowledge Machine Building Blocks
2.5.1 What Are Knowledge Machines?2.5.2 Knowledge-Machine-Based Wisdom Machines; 2.5.3 Sensor-Scanner-Based Wisdom Machines; 2.5.4 Bus Configurations and Switch Locations; 2.6 Machine Clusters; 2.6.1 Single-Wisdom Single-Machine Systems; 2.6.2 Single-Wisdom Multiple-Machine Systems; 2.6.3 Multiple-Wisdom Single-Machine Systems; 2.6.4 Multiple-Wisdom Multiple-Machine Systems; 2.7 From Wisdom to Behavior; 2.8 Order, Awareness, and Search; 2.9 Conclusions; References; 3 General Theory of Knowledge; Chapter Summary; 3.1 A Basis for the Theory of Knowledge; 3.2 Comprehension, Nature, and Knowledge
3.2.1 A Functional Approach3.2.2 Incremental Changes; 3.2.3 Elemental Convolution and Knowledge Operations; 3.3 Central Processing and Knowledge Processing; 3.4 Accumulation of Information, Knowledge, and Wisdom; 3.5 The Enhanced Knowledge Trail; 3.6 Sequencing of Events at Nodes; 3.7 Transitions at I, K, and C Nodes; 3.8 Transition Management at Nodes; 3.9 An Inverse Universe; 3.10 Origin and Destination; 3.10.1 Nature, Origin of Knowledge Trail; 3.10.2 Two Destinations of Knowledge Trail; 3.10.3 Multiple Feedbacks along the Knowledge Trail; 3.10.4 Dynamics of Knowledge in Societies
3.10.5 I, K, C, W, and E Bases to Replace Nodes
Record Nr. UNINA-9910139754403321
Ahamed Syed V. <1938->  
Hoboken, N.J., : John Wiley & Sons, c2009
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Computational framework for knowledge [[electronic resource] ] : integrated behavior of machines / / Syed V. Ahamed
Computational framework for knowledge [[electronic resource] ] : integrated behavior of machines / / Syed V. Ahamed
Autore Ahamed Syed V. <1938->
Pubbl/distr/stampa Hoboken, N.J., : John Wiley & Sons, c2009
Descrizione fisica 1 online resource (568 p.)
Disciplina 006.3
006.3/12
Soggetto topico Data mining
Web databases
Knowledge acquisition (Expert systems)
ISBN 1-282-27896-7
9786612278969
0-470-48042-4
0-470-48041-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto COMPUTATIONAL FRAMEWORK FOR KNOWLEDGE; CONTENTS; Foreword; Preface; Introduction; 1 New Knowledge Environments; Chapter Summary; 1.1 The Need to Know; 1.1.1 Global Power of Knowledge; 1.1.2 Scientific Aspects; 1.1.3 Wealth Aspects; 1.2 Role of Technology; 1.2.1 Three Major Contributions; 1.2.2 A String of Secondary Contributions; 1.2.3 Peripheral Contributions; 1.3 Knowledge and Wealth; 1.4 Evolving Knowledge Environments; 1.4.1 Components of Knowledge; 1.4.2 The Processing of Knowledge; 1.5 Structure and Communication of Knowledge; 1.5.1 Velocity of Flow of Knowledge
1.5.2 Truisms in the Knowledge Domain1.5.3 Philosophic Validation of Knowledge; 1.5.4 Scientific Principles in the Knowledge Domain; 1.5.5 Aspects of Knowledge; 1.6 Intelligent Internet and Knowledge Society; 1.6.1 Four Precursors of Modern Wisdom; 1.6.2 Knowledge Bases to Derive Wisdom; 1.6.3 Role of National Governments; 1.6.4 Universal Knowledge-Processing Systems; 1.6.5 Educational Networks; 1.6.6 Medical Networks; 1.6.7 Antiterrorism Networks; 1.7 Knowledge Networks; 1.7.1 Evolution of Knowledge Networks; 1.7.2 Knowledge Network Configuration; 1.8 Conclusions; References
2 Wisdom MachinesChapter Summary; 2.1 Many "Flavors" of Wisdom; 2.2 Three Orientations of Wisdom; 2.2.1 Absolute Wisdom; 2.2.2 Materialistic Wisdom; 2.2.3 Opportunistic Wisdom; 2.2.4 Needs and Wisdom; 2.2.5 What Are Wisdom Machines?; 2.3 Optimization of Wise Choices; 2.3.1 Derived Axioms; 2.3.2 Priming of Machine Wisdom for Directional Axioms; 2.4 Three-Level Functions; 2.4.1 Level I: Access and Administrative Functions; 2.4.2 Level II: Linkage, Scientific, and Statistical Functions; 2.4.3 Level III: Human Authentication; 2.5 Knowledge Machine Building Blocks
2.5.1 What Are Knowledge Machines?2.5.2 Knowledge-Machine-Based Wisdom Machines; 2.5.3 Sensor-Scanner-Based Wisdom Machines; 2.5.4 Bus Configurations and Switch Locations; 2.6 Machine Clusters; 2.6.1 Single-Wisdom Single-Machine Systems; 2.6.2 Single-Wisdom Multiple-Machine Systems; 2.6.3 Multiple-Wisdom Single-Machine Systems; 2.6.4 Multiple-Wisdom Multiple-Machine Systems; 2.7 From Wisdom to Behavior; 2.8 Order, Awareness, and Search; 2.9 Conclusions; References; 3 General Theory of Knowledge; Chapter Summary; 3.1 A Basis for the Theory of Knowledge; 3.2 Comprehension, Nature, and Knowledge
3.2.1 A Functional Approach3.2.2 Incremental Changes; 3.2.3 Elemental Convolution and Knowledge Operations; 3.3 Central Processing and Knowledge Processing; 3.4 Accumulation of Information, Knowledge, and Wisdom; 3.5 The Enhanced Knowledge Trail; 3.6 Sequencing of Events at Nodes; 3.7 Transitions at I, K, and C Nodes; 3.8 Transition Management at Nodes; 3.9 An Inverse Universe; 3.10 Origin and Destination; 3.10.1 Nature, Origin of Knowledge Trail; 3.10.2 Two Destinations of Knowledge Trail; 3.10.3 Multiple Feedbacks along the Knowledge Trail; 3.10.4 Dynamics of Knowledge in Societies
3.10.5 I, K, C, W, and E Bases to Replace Nodes
Record Nr. UNINA-9910831052603321
Ahamed Syed V. <1938->  
Hoboken, N.J., : John Wiley & Sons, c2009
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Data analysis and data mining [[electronic resource] ] : an introduction / / Adelchi Azzalini and Bruno Scarpa ; [text revised by Gabriel Walton]
Data analysis and data mining [[electronic resource] ] : an introduction / / Adelchi Azzalini and Bruno Scarpa ; [text revised by Gabriel Walton]
Autore Azzalini Adelchi
Pubbl/distr/stampa Oxford ; ; New York, : Oxford University Press, c2012
Descrizione fisica 1 online resource (289 p.)
Disciplina 006.3/12
Altri autori (Persone) ScarpaBruno
WaltonGabriel
Soggetto topico Data mining
Soggetto genere / forma Electronic books.
ISBN 1-280-59575-2
9786613625588
0-19-990928-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Contents; Preface; Preface to the English Edition; 1. Introduction; 1.1. New problems and new opportunities; 1.2. All models are wrong; 1.3. A matter of style; 2. A-B-C; 2.1. Old friends: Linear models; 2.2. Computational aspects; 2.3. Likelihood; 2.4. Logistic regression and GLM; Exercises; 3. Optimism, Conflicts, and Trade-offs; 3.1. Matching the conceptual frame and real life; 3.2. A simple prototype problem; 3.3. If we knew f (x). . .; 3.4. But as we do not know f (x). . .; 3.5. Methods for model selection; 3.6. Reduction of dimensions and selection of most appropriate model
Exercises4. Prediction of Quantitative Variables; 4.1. Nonparametric estimation: Why?; 4.2. Local regression; 4.3. The curse of dimensionality; 4.4. Splines; 4.5. Additive models and GAM; 4.6. Projection pursuit; 4.7. Inferential aspects; 4.8. Regression trees; 4.9. Neural networks; 4.10. Case studies; Exercises; 5. Methods of Classification; 5.1. Prediction of categorical variables; 5.2. An introduction based on a marketing problem; 5.3. Extension to several categories; 5.4. Classification via linear regression; 5.5. Discriminant analysis; 5.6. Some nonparametric methods
5.7. Classification trees5.8. Some other topics; 5.9. Combination of classifiers; 5.10. Case studies; Exercises; 6. Methods of Internal Analysis; 6.1. Cluster analysis; 6.2. Associations among variables; 6.3. Case study: Web usage mining; Appendix A: Complements of Mathematics and Statistics; A.1. Concepts on linear algebra; A.2. Concepts of probability theory; A.3. Concepts of linear models; Appendix B: Data Sets; B.1. Simulated data; B.2. Car data; B.3. Brazilian bank data; B.4. Data for telephone company customers; B.5. Insurance data; B.6. Choice of fruit juice data
B.7. Customer satisfactionB.8. Web usage data; Appendix C: Symbols and Acronyms; References; Author Index; A; B; C; D; E; F; G; H; I; J; K; L; M; N; O; P; Q; R; S; T; V; W; Z; Subject Index; A; B; C; D; E; F; G; H; I; K; L; M; N; O; P; Q; R; S; T; U; V; W
Record Nr. UNINA-9910452304903321
Azzalini Adelchi  
Oxford ; ; New York, : Oxford University Press, c2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui