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Titre : Biostatistiques au quotidien Type de document : texte imprimé Auteurs : Michel Huguier (1937-....), Auteur ; Antoine Flahault, Auteur Editeur : Amsterdam : Elsevier Année de publication : 2000 Importance : 1 vol. (204 p.) Présentation : couv. ill. Format : 24 cm ISBN/ISSN/EAN : 978-2-84299-174-6 Note générale : Bibliogr. p. 195-197. Index Langues : Français (fre) Catégories : Mathématique Mots-clés : Biométrie
Statistique médicale
Méthodes épidémiologiques
Résultat thérapeutique
Biométrie
BiostatistiqueIndex. décimale : 570.151 95 Biostatistique Résumé :
Il existe des guides pour la rédaction d'articles scientifiques, notamment médicaux... Il manquait un bréviaire de biostatistiques... Avec ce livre, Biostatistiques au quotidien, destiné, entre autres, aux médecins, biologistes et étudiants, Michel Huguier, professeur de chirurgie digestive, déjà coauteur d'un livre portant sur la rédaction médicale, et Antoine Flahault, maître de conférences en biostatistiques et informatique, comblent cette lacune. Pour un auteur, concevoir une étude, établir le protocole expérimental, choisir une méthode d'analyse statistique des résultats, bien comprendre les concepts sur lesquels s'appuient ces méthodes, retrouver rapidement la formule mathématique oubliée ou découvrir celle que l'on ne connaissait pas et la comprendre sont les premiers objectifs de ce livre. Pour les lecteurs, apprendre à juger efficacement le contenu des articles médicaux ou scientifiques grâce à la connaissance des statistiques est un autre objectif de ce livre. Les investigateurs en recherche clinique, les chercheurs dans des disciplines plus fondamentales, les étudiants en médecine, comme tous les lecteurs d'articles originaux en sciences de la vie voudront garder ce livre à portée de la main, tant son contenu deviendra précieux dans leur pratique quotidienne.Note de contenu :
Sommaire
Les comparaisons
Concordance et mesures des risques
Principes généraux de l'évaluation
L'évaluation diagnostique
L'évaluation thérapeutique
L'évaluation d'un pronostic
Enquêtes épidémiologiquesCôte titre : Fs/0355 Biostatistiques au quotidien [texte imprimé] / Michel Huguier (1937-....), Auteur ; Antoine Flahault, Auteur . - Amsterdam : Elsevier, 2000 . - 1 vol. (204 p.) : couv. ill. ; 24 cm.
ISBN : 978-2-84299-174-6
Bibliogr. p. 195-197. Index
Langues : Français (fre)
Catégories : Mathématique Mots-clés : Biométrie
Statistique médicale
Méthodes épidémiologiques
Résultat thérapeutique
Biométrie
BiostatistiqueIndex. décimale : 570.151 95 Biostatistique Résumé :
Il existe des guides pour la rédaction d'articles scientifiques, notamment médicaux... Il manquait un bréviaire de biostatistiques... Avec ce livre, Biostatistiques au quotidien, destiné, entre autres, aux médecins, biologistes et étudiants, Michel Huguier, professeur de chirurgie digestive, déjà coauteur d'un livre portant sur la rédaction médicale, et Antoine Flahault, maître de conférences en biostatistiques et informatique, comblent cette lacune. Pour un auteur, concevoir une étude, établir le protocole expérimental, choisir une méthode d'analyse statistique des résultats, bien comprendre les concepts sur lesquels s'appuient ces méthodes, retrouver rapidement la formule mathématique oubliée ou découvrir celle que l'on ne connaissait pas et la comprendre sont les premiers objectifs de ce livre. Pour les lecteurs, apprendre à juger efficacement le contenu des articles médicaux ou scientifiques grâce à la connaissance des statistiques est un autre objectif de ce livre. Les investigateurs en recherche clinique, les chercheurs dans des disciplines plus fondamentales, les étudiants en médecine, comme tous les lecteurs d'articles originaux en sciences de la vie voudront garder ce livre à portée de la main, tant son contenu deviendra précieux dans leur pratique quotidienne.Note de contenu :
Sommaire
Les comparaisons
Concordance et mesures des risques
Principes généraux de l'évaluation
L'évaluation diagnostique
L'évaluation thérapeutique
L'évaluation d'un pronostic
Enquêtes épidémiologiquesCôte titre : Fs/0355 Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité Fs/0355 Fs/0355 Livre Bibliothéque des sciences Français Disponible
Disponible
Titre : Computer architecture : A quantitative approach Type de document : texte imprimé Auteurs : John L. Hennessy (1952-....), Auteur ; David A. Patterson (1947-....), Auteur Mention d'édition : 5th edition Editeur : Waltham (Mass.) : Morgan Kaufmann Année de publication : 2012 Autre Editeur : Amsterdam : Elsevier Importance : 1 vol. (493-[325] p.) Présentation : ill., couv. ill. en coul. Format : 24 cm ISBN/ISSN/EAN : 978-0-12-383872-8 Langues : Anglais (eng) Catégories : Informatique Index. décimale : 004.2 Analyse, conception et évaluation des systèmes informatiques Côte titre : Fs/19721 Computer architecture : A quantitative approach [texte imprimé] / John L. Hennessy (1952-....), Auteur ; David A. Patterson (1947-....), Auteur . - 5th edition . - Waltham (Mass.) : Morgan Kaufmann : Amsterdam : Elsevier, 2012 . - 1 vol. (493-[325] p.) : ill., couv. ill. en coul. ; 24 cm.
ISBN : 978-0-12-383872-8
Langues : Anglais (eng)
Catégories : Informatique Index. décimale : 004.2 Analyse, conception et évaluation des systèmes informatiques Côte titre : Fs/19721 Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité Fs/19721 Fs/19721 Livre Bibliothéque des sciences Français Disponible
Disponible
Titre : Data mining : concepts and techniques Type de document : texte imprimé Auteurs : Jiawei Han, Auteur ; Micheline Kamber, Auteur ; Jian Pei, Auteur Mention d'édition : 3th edition Editeur : Amsterdam : Elsevier Année de publication : 2012 Autre Editeur : Boston : Morgan Kaufmann Importance : 1 vol. (703 p.) Présentation : ill., tabl., graph., couv. ill. en coul. Format : 24 cm ISBN/ISSN/EAN : 978-0-12-381479-1 Langues : Anglais (eng) Catégories : Informatique Mots-clés : Exploration de données
Data miningIndex. décimale : 004 Informatique Résumé :
Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining.
This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining.Note de contenu :
Table of Contents
1. Introduction
1.1 Why Data Mining?
1.2 What Is Data Mining?
1.3 What Kinds of Data Can Be Mined?
1.4 What Kinds of Patterns Can Be Mined?
1.5 Which Technologies Are Used?
1.6 Which Kinds of Applications Are Targeted?
1.7 Major Issues in Data Mining
1.8 Summary
1.9 Exercises
1.10 Bibliographic Notes
2. Getting to Know Your Data
Publisher Summary
2.1 Data Objects and Attribute Types
2.2 Basic Statistical Descriptions of Data
2.3 Data Visualization
2.4 Measuring Data Similarity and Dissimilarity
2.5 Summary
2.6 Exercises
2.7 Bibliographic Notes
3. Data Preprocessing
3.1 Data Preprocessing: An Overview
3.2 Data Cleaning
3.3 Data Integration
3.4 Data Reduction
3.5 Data Transformation and Data Discretization
3.6 Summary
3.7 Exercises
3.8 Bibliographic Notes
4. Data Warehousing and Online Analytical Processing
4.1 Data Warehouse: Basic Concepts
4.2 Data Warehouse Modeling: Data Cube and OLAP
4.3 Data Warehouse Design and Usage
4.4 Data Warehouse Implementation
4.5 Data Generalization by Attribute-Oriented Induction
4.6 Summary
4.7 Exercises
Bibliographic Notes
5. Data Cube Technology
5.1 Data Cube Computation: Preliminary Concepts
5.2 Data Cube Computation Methods
5.3 Processing Advanced Kinds of Queries by Exploring Cube Technology
5.4 Multidimensional Data Analysis in Cube Space
5.5 Summary
5.6 Exercises
5.7 Bibliographic Notes
6. Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods
Publisher Summary
6.1 Basic Concepts
6.2 Frequent Itemset Mining Methods
6.3 Which Patterns Are Interesting?—Pattern Evaluation Methods
6.4 Summary
6.5 Exercises
6.6 Bibliographic Notes
7. Advanced Pattern Mining
7.1 Pattern Mining: A Road Map
7.2 Pattern Mining in Multilevel, Multidimensional Space
7.3 Constraint-Based Frequent Pattern Mining
7.4 Mining High-Dimensional Data and Colossal Patterns
7.5 Mining Compressed or Approximate Patterns
7.6 Pattern Exploration and Application
7.7 Summary
7.8 Exercises
7.9 Bibliographic Notes
8. Classification: Basic Concepts
8.1 Basic Concepts
8.2 Decision Tree Induction
8.3 Bayes Classification Methods
8.4 Rule-Based Classification
8.5 Model Evaluation and Selection
8.6 Techniques to Improve Classification Accuracy
8.7 Summary
8.8 Exercises
8.9 Bibliographic Notes
9. Classification: Advanced Methods
9.1 Bayesian Belief Networks
9.2 Classification by Backpropagation
9.3 Support Vector Machines
9.4 Classification Using Frequent Patterns
9.5 Lazy Learners (or Learning from Your Neighbors)
9.6 Other Classification Methods
9.7 Additional Topics Regarding Classification
Summary
9.9 Exercises
9.10 Bibliographic Notes
10. Cluster Analysis: Basic Concepts and Methods
10.1 Cluster Analysis
10.2 Partitioning Methods
10.3 Hierarchical Methods
10.4 Density-Based Methods
10.5 Grid-Based Methods
10.6 Evaluation of Clustering
10.7 Summary
10.8 Exercises
10.9 Bibliographic Notes
11. Advanced Cluster Analysis
11.1 Probabilistic Model-Based Clustering
11.2 Clustering High-Dimensional Data
11.3 Clustering Graph and Network Data
11.4 Clustering with Constraints
Summary
11.6 Exercises
11.7 Bibliographic Notes
12. Outlier Detection
12.1 Outliers and Outlier Analysis
12.2 Outlier Detection Methods
12.3 Statistical Approaches
12.4 Proximity-Based Approaches
12.5 Clustering-Based Approaches
12.6 Classification-Based Approaches
12.7 Mining Contextual and Collective Outliers
12.8 Outlier Detection in High-Dimensional Data
12.9 Summary
12.10 Exercises
12.11 Bibliographic Notes
13. Data Mining Trends and Research Frontiers
13.1 Mining Complex Data Types
13.2 Other Methodologies of Data Mining
13.3 Data Mining Applications
13.4 Data Mining and Society
13.5 Data Mining Trends
13.6 Summary
13.7 Exercises
13.8 Bibliographic Notes
Bibliography
IndexCôte titre : Fs/19727 En ligne : https://ia800702.us.archive.org/7/items/datamining_201811/DS-book%20u5.pdf Data mining : concepts and techniques [texte imprimé] / Jiawei Han, Auteur ; Micheline Kamber, Auteur ; Jian Pei, Auteur . - 3th edition . - Amsterdam : Elsevier : Boston : Morgan Kaufmann, 2012 . - 1 vol. (703 p.) : ill., tabl., graph., couv. ill. en coul. ; 24 cm.
ISBN : 978-0-12-381479-1
Langues : Anglais (eng)
Catégories : Informatique Mots-clés : Exploration de données
Data miningIndex. décimale : 004 Informatique Résumé :
Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining.
This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining.Note de contenu :
Table of Contents
1. Introduction
1.1 Why Data Mining?
1.2 What Is Data Mining?
1.3 What Kinds of Data Can Be Mined?
1.4 What Kinds of Patterns Can Be Mined?
1.5 Which Technologies Are Used?
1.6 Which Kinds of Applications Are Targeted?
1.7 Major Issues in Data Mining
1.8 Summary
1.9 Exercises
1.10 Bibliographic Notes
2. Getting to Know Your Data
Publisher Summary
2.1 Data Objects and Attribute Types
2.2 Basic Statistical Descriptions of Data
2.3 Data Visualization
2.4 Measuring Data Similarity and Dissimilarity
2.5 Summary
2.6 Exercises
2.7 Bibliographic Notes
3. Data Preprocessing
3.1 Data Preprocessing: An Overview
3.2 Data Cleaning
3.3 Data Integration
3.4 Data Reduction
3.5 Data Transformation and Data Discretization
3.6 Summary
3.7 Exercises
3.8 Bibliographic Notes
4. Data Warehousing and Online Analytical Processing
4.1 Data Warehouse: Basic Concepts
4.2 Data Warehouse Modeling: Data Cube and OLAP
4.3 Data Warehouse Design and Usage
4.4 Data Warehouse Implementation
4.5 Data Generalization by Attribute-Oriented Induction
4.6 Summary
4.7 Exercises
Bibliographic Notes
5. Data Cube Technology
5.1 Data Cube Computation: Preliminary Concepts
5.2 Data Cube Computation Methods
5.3 Processing Advanced Kinds of Queries by Exploring Cube Technology
5.4 Multidimensional Data Analysis in Cube Space
5.5 Summary
5.6 Exercises
5.7 Bibliographic Notes
6. Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods
Publisher Summary
6.1 Basic Concepts
6.2 Frequent Itemset Mining Methods
6.3 Which Patterns Are Interesting?—Pattern Evaluation Methods
6.4 Summary
6.5 Exercises
6.6 Bibliographic Notes
7. Advanced Pattern Mining
7.1 Pattern Mining: A Road Map
7.2 Pattern Mining in Multilevel, Multidimensional Space
7.3 Constraint-Based Frequent Pattern Mining
7.4 Mining High-Dimensional Data and Colossal Patterns
7.5 Mining Compressed or Approximate Patterns
7.6 Pattern Exploration and Application
7.7 Summary
7.8 Exercises
7.9 Bibliographic Notes
8. Classification: Basic Concepts
8.1 Basic Concepts
8.2 Decision Tree Induction
8.3 Bayes Classification Methods
8.4 Rule-Based Classification
8.5 Model Evaluation and Selection
8.6 Techniques to Improve Classification Accuracy
8.7 Summary
8.8 Exercises
8.9 Bibliographic Notes
9. Classification: Advanced Methods
9.1 Bayesian Belief Networks
9.2 Classification by Backpropagation
9.3 Support Vector Machines
9.4 Classification Using Frequent Patterns
9.5 Lazy Learners (or Learning from Your Neighbors)
9.6 Other Classification Methods
9.7 Additional Topics Regarding Classification
Summary
9.9 Exercises
9.10 Bibliographic Notes
10. Cluster Analysis: Basic Concepts and Methods
10.1 Cluster Analysis
10.2 Partitioning Methods
10.3 Hierarchical Methods
10.4 Density-Based Methods
10.5 Grid-Based Methods
10.6 Evaluation of Clustering
10.7 Summary
10.8 Exercises
10.9 Bibliographic Notes
11. Advanced Cluster Analysis
11.1 Probabilistic Model-Based Clustering
11.2 Clustering High-Dimensional Data
11.3 Clustering Graph and Network Data
11.4 Clustering with Constraints
Summary
11.6 Exercises
11.7 Bibliographic Notes
12. Outlier Detection
12.1 Outliers and Outlier Analysis
12.2 Outlier Detection Methods
12.3 Statistical Approaches
12.4 Proximity-Based Approaches
12.5 Clustering-Based Approaches
12.6 Classification-Based Approaches
12.7 Mining Contextual and Collective Outliers
12.8 Outlier Detection in High-Dimensional Data
12.9 Summary
12.10 Exercises
12.11 Bibliographic Notes
13. Data Mining Trends and Research Frontiers
13.1 Mining Complex Data Types
13.2 Other Methodologies of Data Mining
13.3 Data Mining Applications
13.4 Data Mining and Society
13.5 Data Mining Trends
13.6 Summary
13.7 Exercises
13.8 Bibliographic Notes
Bibliography
IndexCôte titre : Fs/19727 En ligne : https://ia800702.us.archive.org/7/items/datamining_201811/DS-book%20u5.pdf Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité Fs/19727 Fs/19727 Livre Bibliothéque des sciences Français Disponible
Sorti jusqu'au 08/05/2024
Titre : Data mining : Practical machine learning tools and techniques Type de document : texte imprimé Auteurs : Ian H. Witten, Auteur ; Eibe Frank, Auteur ; Mark A. Hall, Auteur ; Christopher J. Pal, Auteur Mention d'édition : Fourth Edition Editeur : Amsterdam : Elsevier Année de publication : 2017 Importance : 1 vol. (621 p.) Format : 24 cm ISBN/ISSN/EAN : 978-0-12-804291-5 Langues : Anglais (eng) Catégories : Informatique Mots-clés : Exploration de données
Bases de données :Interrogation
Bases de données
Exploration de données
Bases de données -- Interrogation
Bases de données
Data miningRésumé :
Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research. Please visit the book companion website at http://www.cs.waikato.ac.nz/ml/weka/book.html. It contains Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the bookOnline Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the bookTable of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etcNote de contenu :
Table of Contents of the 4th Edition:
Preface
1. What’s it all about?
1.1 Data Mining and Machine Learning
1.2 Simple Examples: The Weather Problem and Others
1.3 Fielded Applications
1.4 The Data Mining Process
1.5 Machine Learning and Statistics
1.6 Generalization as Search
1.7 Data Mining and Ethics
1.8 Further Reading and Bibliographic Notes
2. Input: concepts, instances, attributes
2.1 What’s a Concept?
2.2 What’s in an Example?
2.3 What’s in an Attribute?
2.4 Preparing the Input
2.5 Further Reading and Bibliographic Notes
3. Output: Knowledge representation
3.1 Tables
3.2 Linear Models
3.3 Trees
3.4 Rules
3.5 Instance-Based Representation
3.6 Clusters
3.7 Further Reading and Bibliographic Notes
4. Algorithms: the basic methods
4.1 Inferring Rudimentary Rules
4.2 Simple Probabilistic Modeling
4.3 Divide-and-Conquer: Constructing Decision Trees
4.4 Covering Algorithms: Constructing Rules
4.5 Mining Association Rules
4.6 Linear Models
4.7 Instance-Based Learning
4.8 Clustering
4.9 Multi-Instance Learning
4.10 Further Reading and Bibliographic Notes
4.11 WEKA Implementations
5. Credibility: Evaluating what’s been learned
5.1 Training and Testing
5.2 Predicting Performance
5.3 Cross-Validation
5.4 Other Estimates
5.5 Hyperparameter Selection
5.6 Comparing Data Mining Schemes
5.7 Predicting Probabilities
5.8 Counting the Cost
5.9 Evaluating Numeric Prediction
5.10 The Minimum Description Length Principle
5.11 Applying MDL to Clustering
5.12 Using a Validation Set for Model Selection
5.13 Further Reading and Bibliographic Notes
6. Trees and rules
6.1 Decision Trees
6.2 Classification Rules
6.3 Association Rules
6.4 WEKA Implementations
7. Extending instance-based and linear models
7.1 Instance-Based Learning
7.2 Extending Linear Models
7.3 Numeric Prediction with Local Linear Models
7.4 WEKA Implementations
8. Data transformations
8.1 Attribute Selection
8.2 Discretizing Numeric Attributes
8.3 Projections
8.4 Sampling
8.5 Cleansing
8.6 Transforming Multiple Classes to Binary Ones
8.7 Calibrating Class Probabilities
8.8 Further Reading and Biblographic Notes
8.9 WEKA Implementations
9. Probabilistic methods
9.1 Foundations
9.2 Bayesian Networks
9.3 Clustering and Probability Density Estimation
9.4 Hidden Variable Models
9.5 Bayesian Estimation and Prediction
9.6 Graphical Models and Factor Graphs
9.7 Conditional Probability Models
9.8 Sequential and Temporal Models
9.9 Further Reading and Bibliographic Notes
9.10 WEKA Implementations
10. Deep learning
10.1 Deep Feedforward Networks
10.2 Training and Evaluating Deep Networks
10.3 Convolutional Neural Networks
10.4 Autoencoders
10.5 Stochastic Deep Networks
10.6 Recurrent Neural Networks
10.7 Further Reading and Bibliographic Notes
10.8 Deep Learning Software and Network Implementations
10.9 WEKA implementations
11. Beyond supervised and unsupervised learning
11.1 Semi-supervised learning
11.2 Multi-instance Learning
11.3 Further Reading and Bibliographic Notes
11.4 WEKA Implementations
12. Ensemble Learning
12.1 Combining Multiple Models
12.2 Bagging
12.3 Randomization
12.4 Boosting
12.5 Additive Regression
12.6 Interpretable Ensembles
12.7 Stacking
12.8 Further Reading and Bibliographic Notes
12.9 WEKA Implementations
13. Moving on: Applications and Beyond
13.1 Applying Data Mining
13.2 Learning from Massive Datasets
13.3 Data Stream Learning
13.4 Incorporating Domain Knowledge
13.5 Text Mining
13.6 Web Mining
13.7 Images and Speech
13.8 Adversarial Situations
13.9 Ubiquitous Data Mining
13.10 Further Reading and Bibliographic Notes
13.11 WEKA Implementations
IndexCôte titre : Fs/19728 En ligne : https://www.amazon.com/exec/obidos/ASIN/0128042915/departmofcompute?asin=0128042 [...] Data mining : Practical machine learning tools and techniques [texte imprimé] / Ian H. Witten, Auteur ; Eibe Frank, Auteur ; Mark A. Hall, Auteur ; Christopher J. Pal, Auteur . - Fourth Edition . - Amsterdam : Elsevier, 2017 . - 1 vol. (621 p.) ; 24 cm.
ISBN : 978-0-12-804291-5
Langues : Anglais (eng)
Catégories : Informatique Mots-clés : Exploration de données
Bases de données :Interrogation
Bases de données
Exploration de données
Bases de données -- Interrogation
Bases de données
Data miningRésumé :
Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research. Please visit the book companion website at http://www.cs.waikato.ac.nz/ml/weka/book.html. It contains Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the bookOnline Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the bookTable of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etcNote de contenu :
Table of Contents of the 4th Edition:
Preface
1. What’s it all about?
1.1 Data Mining and Machine Learning
1.2 Simple Examples: The Weather Problem and Others
1.3 Fielded Applications
1.4 The Data Mining Process
1.5 Machine Learning and Statistics
1.6 Generalization as Search
1.7 Data Mining and Ethics
1.8 Further Reading and Bibliographic Notes
2. Input: concepts, instances, attributes
2.1 What’s a Concept?
2.2 What’s in an Example?
2.3 What’s in an Attribute?
2.4 Preparing the Input
2.5 Further Reading and Bibliographic Notes
3. Output: Knowledge representation
3.1 Tables
3.2 Linear Models
3.3 Trees
3.4 Rules
3.5 Instance-Based Representation
3.6 Clusters
3.7 Further Reading and Bibliographic Notes
4. Algorithms: the basic methods
4.1 Inferring Rudimentary Rules
4.2 Simple Probabilistic Modeling
4.3 Divide-and-Conquer: Constructing Decision Trees
4.4 Covering Algorithms: Constructing Rules
4.5 Mining Association Rules
4.6 Linear Models
4.7 Instance-Based Learning
4.8 Clustering
4.9 Multi-Instance Learning
4.10 Further Reading and Bibliographic Notes
4.11 WEKA Implementations
5. Credibility: Evaluating what’s been learned
5.1 Training and Testing
5.2 Predicting Performance
5.3 Cross-Validation
5.4 Other Estimates
5.5 Hyperparameter Selection
5.6 Comparing Data Mining Schemes
5.7 Predicting Probabilities
5.8 Counting the Cost
5.9 Evaluating Numeric Prediction
5.10 The Minimum Description Length Principle
5.11 Applying MDL to Clustering
5.12 Using a Validation Set for Model Selection
5.13 Further Reading and Bibliographic Notes
6. Trees and rules
6.1 Decision Trees
6.2 Classification Rules
6.3 Association Rules
6.4 WEKA Implementations
7. Extending instance-based and linear models
7.1 Instance-Based Learning
7.2 Extending Linear Models
7.3 Numeric Prediction with Local Linear Models
7.4 WEKA Implementations
8. Data transformations
8.1 Attribute Selection
8.2 Discretizing Numeric Attributes
8.3 Projections
8.4 Sampling
8.5 Cleansing
8.6 Transforming Multiple Classes to Binary Ones
8.7 Calibrating Class Probabilities
8.8 Further Reading and Biblographic Notes
8.9 WEKA Implementations
9. Probabilistic methods
9.1 Foundations
9.2 Bayesian Networks
9.3 Clustering and Probability Density Estimation
9.4 Hidden Variable Models
9.5 Bayesian Estimation and Prediction
9.6 Graphical Models and Factor Graphs
9.7 Conditional Probability Models
9.8 Sequential and Temporal Models
9.9 Further Reading and Bibliographic Notes
9.10 WEKA Implementations
10. Deep learning
10.1 Deep Feedforward Networks
10.2 Training and Evaluating Deep Networks
10.3 Convolutional Neural Networks
10.4 Autoencoders
10.5 Stochastic Deep Networks
10.6 Recurrent Neural Networks
10.7 Further Reading and Bibliographic Notes
10.8 Deep Learning Software and Network Implementations
10.9 WEKA implementations
11. Beyond supervised and unsupervised learning
11.1 Semi-supervised learning
11.2 Multi-instance Learning
11.3 Further Reading and Bibliographic Notes
11.4 WEKA Implementations
12. Ensemble Learning
12.1 Combining Multiple Models
12.2 Bagging
12.3 Randomization
12.4 Boosting
12.5 Additive Regression
12.6 Interpretable Ensembles
12.7 Stacking
12.8 Further Reading and Bibliographic Notes
12.9 WEKA Implementations
13. Moving on: Applications and Beyond
13.1 Applying Data Mining
13.2 Learning from Massive Datasets
13.3 Data Stream Learning
13.4 Incorporating Domain Knowledge
13.5 Text Mining
13.6 Web Mining
13.7 Images and Speech
13.8 Adversarial Situations
13.9 Ubiquitous Data Mining
13.10 Further Reading and Bibliographic Notes
13.11 WEKA Implementations
IndexCôte titre : Fs/19728 En ligne : https://www.amazon.com/exec/obidos/ASIN/0128042915/departmofcompute?asin=0128042 [...] Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité Fs/19728 Fs/19728 Livre Bibliothéque des sciences Français Disponible
Sorti jusqu'au 08/05/2024
Titre : Statistical Mechanics :An advanced course with problems and solutions Type de document : texte imprimé Auteurs : Ryōgo Kubo Editeur : Amsterdam : Elsevier Année de publication : 1965 Collection : North-Holland personal library Importance : 1 vol. (425 p.) Présentation : ill. Format : 24 cm ISBN/ISSN/EAN : 978-0-444-10224-9 Langues : Anglais (eng) Catégories : Physique Mots-clés : Physique Index. décimale : 530.13 - Mécanique statistique et physique statistique, Résumé :
La physique moderne a ouvert la voie à un examen approfondi de l'infrastructure de la nature et à la compréhension des propriétés de la matière d'un point de vue atomistique. La mécanique statistique est un pont essentiel entre les lois de la nature à l'échelle microscopique et le comportement macroscopique de la matière. Une bonne formation en mécanique statistique constitue donc une base pour la physique moderne et est indispensable à tout étudiant en physique, chimie, biophysique et sciences de l'ingénieur qui souhaite travailler dans ces domaines scientifiques et technologiques en plein essor.Côte titre : Fs/10345 Statistical Mechanics :An advanced course with problems and solutions [texte imprimé] / Ryōgo Kubo . - Amsterdam : Elsevier, 1965 . - 1 vol. (425 p.) : ill. ; 24 cm. - (North-Holland personal library) .
ISBN : 978-0-444-10224-9
Langues : Anglais (eng)
Catégories : Physique Mots-clés : Physique Index. décimale : 530.13 - Mécanique statistique et physique statistique, Résumé :
La physique moderne a ouvert la voie à un examen approfondi de l'infrastructure de la nature et à la compréhension des propriétés de la matière d'un point de vue atomistique. La mécanique statistique est un pont essentiel entre les lois de la nature à l'échelle microscopique et le comportement macroscopique de la matière. Une bonne formation en mécanique statistique constitue donc une base pour la physique moderne et est indispensable à tout étudiant en physique, chimie, biophysique et sciences de l'ingénieur qui souhaite travailler dans ces domaines scientifiques et technologiques en plein essor.Côte titre : Fs/10345 Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité Fs/10345 Fs/10345 livre Bibliothéque des sciences Anglais Disponible
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