Book

Voltaire œuvres complètes

  • Auteur: Voltaire
  • ISBN:
  • Rayon: V
  • Maison Edition: Arvensa
  • Ville Edition:
  • Année Edition: sd
  • Domaine: Linguistique

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Book

SÉMANTIQUE Interprétative

  • Auteur: FRANÇOIS RASTIER
  • ISBN: 978-2-13-057495-8
  • Rayon: R
  • Maison Edition: PUF
  • Ville Edition: Paris
  • Année Edition: 1987
  • Domaine: Linguistique

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Book

Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining

  • Auteur: Hassan AbouEisha, Talha Amin, Igor Chikalov, Shahid Hussain, Mikhail Moshkov
  • ISBN: 978-3-319-91839-6
  • Rayon: A
  • Maison Edition: Springer
  • Ville Edition:
  • Année Edition: 2019
  • Domaine: Intelligence Artificielle

Dynamic programming is an efficient technique for solving optimization problems. It is based on breaking the initial problem down into simpler ones and solving these sub-problems, beginning with the simplest ones. A conventional dynamic programming algorithm returns an optimal object from a given set of objects. This book develops extensions of dynamic programming, enabling us to (i) describe the set of objects under consideration; (ii) perform a multi-stage optimization of objects relative to different criteria; (iii) count the number of optimal objects; (iv) find the set of Pareto optimal points for bi-criteria optimization problems; and (v) to study relationships between two criteria. It considers various applications, including optimization of decision trees and decision rule systems as algorithms for problem solving, as ways for knowledge representation, and as classifiers; optimization of element partition trees for rectangular meshes, which are used in finite element methods for solving PDEs; and multi-stage optimization for such classic combinatorial optimization problems as matrix chain multiplication, binary search trees, global sequence alignment, and shortest paths. The results presented are useful for researchers in combinatorial optimization, data mining, knowledge discovery, machine learning, and finite element methods, especially those working in rough set theory, test theory, logical analysis of data, and PDE solvers. This book can be used as the basis for graduate courses.

Book

R: Mining Spatial, Text, Web, and Social Media Data

  • Auteur: Bater Makhabel, Pradeepta Mishra, Nathan Danneman, Richard Heimann
  • ISBN: 978-1-78829-374-7
  • Rayon: M
  • Maison Edition: Packt
  • Ville Edition: BIRMINGHAM - MUMBAI
  • Année Edition: 2017
  • Domaine: Intelligence Artificielle

Develop a strong strategy to solve predictive modeling problems using the most popular data mining algorithms Real-world case studies will take you from novice to intermediate to apply data mining techniques Deploy cutting-edge sentiment analysis techniques to real-world social media data using R

Book

Data Mining for Systems Biology

  • Auteur: Hiroshi Mamitsuka
  • ISBN: 978-1-4939-8561-6
  • Rayon: M
  • Maison Edition: Humana Press
  • Ville Edition: New York
  • Année Edition: 2018
  • Domaine: Sciences Informatiques

This fully updated book collects numerous data mining techniques, reflecting the acceleration and diversity of the development of data-driven approaches to the life sciences. The first half of the volume examines genomics, particularly metagenomics and epigenomics, which promise to deepen our knowledge of genes and genomes, while the second half of the book emphasizes metabolism and the metabolome as well as relevant medicine-oriented subjects. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that is useful for getting optimal results. Authoritative and practical, Data Mining for Systems Biology: Methods and Protocols, Second Edition serves as an ideal resource for researchers of biology and relevant fields, such as medical, pharmaceutical, and agricultural sciences, as well as for the scientists and engineers who are working on developing data-driven techniques, such as databases, data sciences, data mining, visualization systems, and machine learning or artificial intelligence that now are central to the paradigm-altering discoveries being made with a higher frequency.

Book

Individual and Collective Graph Mining: Principles, Algorithms, and Applications

  • Auteur: Danai Koutra, Christos Faloutsos, Jiawei Han
  • ISBN: 9781681730400
  • Rayon: K
  • Maison Edition: Morgan & Claypool
  • Ville Edition:
  • Année Edition: 2018
  • Domaine: Intelligence Artificielle

Graphs naturally represent information ranging from links between web pages, to communication in email networks, to connections between neurons in our brains. These graphs often span billions of nodes and interactions between them. Within this deluge of interconnected data, how can we find the most important structures and summarize them? How can we efficiently visualize them? How can we detect anomalies that indicate critical events, such as an attack on a computer system, disease formation in the human brain, or the fall of a company? This book presents scalable, principled discovery algorithms that combine globality with locality to make sense of one or more graphs. In addition to fast algorithmic methodologies, we also contribute graph-theoretical ideas and models, and real-world applications in two main areas: •Individual Graph Mining: We show how to interpretably summarize a single graph by identifying its important graph structures. We complement summarization with inference, which leverages information about few entities (obtained via summarization or other methods) and the network structure to efficiently and effectively learn information about the unknown entities. •Collective Graph Mining: We extend the idea of individual-graph summarization to time-evolving graphs, and show how to scalably discover temporal patterns. Apart from summarization, we claim that graph similarity is often the underlying problem in a host of applications where multiple graphs occur (e.g., temporal anomaly detection, discovery of behavioral patterns), and we present principled, scalable algorithms for aligning networks and measuring their similarity. The methods that we present in this book leverage techniques from diverse areas, such as matrix algebra, graph theory, optimization, information theory, machine learning, finance, and social science, to solve real-world problems. We present applications of our exploration algorithms to massive datasets, including a Web graph of 6.6 billion edges, a Twitter graph of 1.8 billion edges, brain graphs with up to 90 million edges, collaboration, peer-to-peer networks, browser logs, all spanning millions of users and interactions.

Book

Exploratory Data Analysis Using R

  • Auteur: Ronald K. Pearson
  • ISBN: 978-1-1384-8060-5
  • Rayon: R
  • Maison Edition: CRC Press
  • Ville Edition:
  • Année Edition: 2018
  • Domaine: Intelligence Artificielle

Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA) and introduces the range of "interesting" – good, bad, and ugly – features that can be found in data, and why it is important to find them. It also introduces the mechanics of using R to explore and explain data. The book begins with a detailed overview of data, exploratory analysis, and R, as well as graphics in R. It then explores working with external data, linear regression models, and crafting data stories. The second part of the book focuses on developing R programs, including good programming practices and examples, working with text data, and general predictive models. The book ends with a chapter on "keeping it all together" that includes managing the R installation, managing files, documenting, and an introduction to reproducible computing. The book is designed for both advanced undergraduate, entry-level graduate students, and working professionals with little to no prior exposure to data analysis, modeling, statistics, or programming. it keeps the treatment relatively non-mathematical, even though data analysis is an inherently mathematical subject. Exercises are included at the end of most chapters, and an instructor's solution manual is available.

Book

Deep Learning Innovations and Their Convergence With Big Data (Advances in Data Mining and Database Management

  • Auteur: S. Karthik, S. Karthik, Anand Paul, N. Karthikeyan
  • ISBN: 9781522530169
  • Rayon: K
  • Maison Edition: IGI Global
  • Ville Edition:
  • Année Edition: 2018
  • Domaine: Intelligence Artificielle

The expansion of digital data has transformed various sectors of business such as healthcare, industrial manufacturing, and transportation. A new way of solving business problems has emerged through the use of machine learning techniques in conjunction with big data analytics. Deep Learning Innovations and Their Convergence With Big Data is a pivotal reference for the latest scholarly research on upcoming trends in data analytics and potential technologies that will facilitate insight in various domains of science, industry, business, and consumer applications. Featuring extensive coverage on a broad range of topics and perspectives such as deep neural network, domain adaptation modeling, and threat detection, this book is ideally designed for researchers, professionals, and students seeking current research on the latest trends in the field of deep learning techniques in big data analytics.

Book

Machine learning and data mining in pattern recognition

  • Auteur: Perner Petra (Ed.)
  • ISBN: 978-3-319-62416-7
  • Rayon: P
  • Maison Edition: Springer
  • Ville Edition: New York
  • Année Edition: 2017
  • Domaine: Intelligence Artificielle

This book constitutes the refereed proceedings of the 13th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2017, held in New York, NY, USA in July/August 2017. The 31 full papers presented in this book were carefully reviewed and selected from 150 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to Read more... Abstract: This book constitutes the refereed proceedings of the 13th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2017, held in New York, NY, USA in July/August 2017. The 31 full papers presented in this book were carefully reviewed and selected from 150 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multi-media data types such as image mining, text mining, video mining, and Web mining