• Book

    Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining

    Auteur: Hassan AbouEisha, Talha Amin, Igor Chikalov, Shahid Hussain, Mikhail Moshkov

    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

    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

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

    • Auteur: Bater Makhabel, Pradeepta Mishra, Nathan Danneman, Richard Heimann
    • ISBN: 978-1-78829-374-7

    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

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

    Auteur: Danai Koutra, Christos Faloutsos, Jiawei Han

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

    • Auteur: Danai Koutra, Christos Faloutsos, Jiawei Han
    • ISBN: 9781681730400

    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

    Exploratory Data Analysis Using R

    • Auteur: Ronald K. Pearson
    • ISBN: 978-1-1384-8060-5

    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

    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

    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.)

    Machine learning and data mining in pattern recognition

    • Auteur: Perner Petra (Ed.)
    • ISBN: 978-3-319-62416-7

    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

  • Book

    L'intelligence artificielle. Dans toutes ses dimensions

    Auteur: Boris Barraud

    L'intelligence artificielle. Dans toutes ses dimensions

    • Auteur: Boris Barraud
    • ISBN: 978-2-343-19274-1

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  • Book

    Intelligence artificielle: Essai de science cognitive

    Auteur: Fondation Prospective et Innovation

    Intelligence artificielle: Essai de science cognitive

    • Auteur: Fondation Prospective et Innovation
    • ISBN:

    Aujourd'hui, l'intelligence artificielle et les technologies qui lui sont liées – internet, les semi-conducteurs, les données, les réseaux mobiles 5G, l'informatique quantique – se développent à grande vitesse. Elles prennent chaque jour davantage d'importance dans notre vie quotidienne et dans nos calculs d'avenir. Elles font figure à bien des égards de « nouvelle frontière » de l'humanité et de champ de bataille privilégié de la compétition pour la prééminence internationale. La Fondation Prospective et Innovation a souhaité consacrer à ce sujet essentiel plusieurs réunions de réflexion avec divers spécialistes français et étrangers. C'est sur cette base que le présent ouvrage a été rédigé. Il s'efforce de décrire de manière simple et équilibrée les différents enjeux de l'intelligence artificielle, qui relèvent aussi bien de l'économie que du social, de la géopolitique que de la morale et, au bout du compte, de la politique tout court. Stephen Hawking disait que l'intelligence artificielle serait la plus grande création de l'humanité mais que, si nous n'y prenions garde, elle pourrait être sa dernière. À nous d'être attentifs et vigilants pour que cette création de l'humanité ne bouche pas l'horizon mais en ouvre de nouveaux.

  • Book

    Intelligence artificielle vulgarisée

    Auteur: Aurélien Vannieuwenhuyze

    Intelligence artificielle vulgarisée

    • Auteur: Aurélien Vannieuwenhuyze
    • ISBN: -

    L'intelligence artificielle est aujourd'hui incontournable. Cependant, les approches pédagogiques réalisées par les ouvrages et sites internet dédiés à l'intelligence artificielle restent souvent complexes. Ce livre a pour objectif de présenter de façon simple et concrète la création de projets basés sur de l'intelligence artificielle en mettant de côté les formules mathématiques et statistiques décourageantes pour la plupart des novices. Il permet ainsi de rendre compréhensibles et applicables les concepts du Machine Learning et du Deep Learning à toute personne âgée de 15 à 99 ans ! La démarche proposée par cet ouvrage se veut progressive et l'auteur entremêle théorie et cas pratiques. Après une introduction à l'intelligence artificielle et aux craintes qu'elle suscite, deux chapitres proposent un bref rappel sur les fondamentaux du langage Python, sur des notions statistiques ainsi qu'une présentation des algorithmes du Machine Learning et de leur champ d'application. Le lecteur peut ensuite, grâce aux trois chapitres qui suivent, découvrir comment donner la faculté à sa machine de prédire des valeurs et de réaliser des classifications. Vient ensuite la découverte de l'apprentissage non supervisé puis de la classification de texte. Enfin, à travers trois chapitres successifs traitant des réseaux de neurones, le lecteur découvre comment les neurosciences ont eu un impact sur l'intelligence artificielle. L'ouvrage se termine par la réalisation de cas pratiques : un premier mêlant réseau de neurones et parole et un second relatif au premier chatBot.

  • Book

    Géopolitique de l'intelligence artificielle : Comment la révolution numérique va bouleverser nos sociétés

    Auteur: Pascal Boniface

    Géopolitique de l'intelligence artificielle : Comment la révolution numérique va bouleverser nos sociétés

    • Auteur: Pascal Boniface
    • ISBN: 978-2-416-00055-3

    Qu'est-ce que l'IA ? Quel enjeu pour l'emploi ? La souveraineté appartient-elle désormais aux GAFA ou bien est-ce au contraire une opportunité pour les citoyens de s'émanciper ? Quelles conséquences géostratégiques ? Autant de questions de fond que cet ouvrage vient éclairer de façon accessible, concise et pédagogique.

  • Book

    L'esprit et la machine

    Auteur: Serge Boisse

    L'esprit et la machine

    • Auteur: Serge Boisse
    • ISBN: -

    Les récentes avancées dans la compréhension du cerveau et dans la programmation d'ordinateurs intelligents, décrites dans ce livre, montrent que nous ne sommes plus très loin de savoir réaliser une Intelligence Artificielle générale, consciente et créative. Mais une telle AI ne pensera pas comme un humain. Elle sera bien plus intelligente, elle pensera bien plus rapidement que nous, et cela ouvre la porte à tous les espoirs comme à toutes les craintes. Que se passe-t-il dans notre cerveau ? Comment fonctionne notre esprit ? Et notre conscience ? Pourra-t-on créer un jour une intelligence artificielle véritable ? Comment la concevoir, quelles seront ses capacités, et devons-nous en avoir peur ? Ce livre répond en détail à toutes ces questions. Précis mais non sans humour, abondamment illustré, ce livre pose clairement la question de ce qui se passera avant, pendant et après cet événement unique, qui sera d’une importance comparable à l’invention du langage, du feu, ou de l’écriture : L'arrivée de l'IA dans le monde des humains.

  • Book

    Data Structures and Algorithms with Python: WithanIntroduction to Multiprocessing

    Auteur: Kent D.Lee and Steve Hubbard

    Data Structures and Algorithms with Python: WithanIntroduction to Multiprocessing

    • Auteur: Kent D.Lee and Steve Hubbard
    • ISBN: 978-3-031-42209-6

    This textbook explains the concepts and techniques required to write programs that can handle large amounts of data efficiently. Project-oriented and classroom-tested, the book presents a number of important algorithms?supported by motivating examples?that bring meaning to the problems faced by computer programmers. The idea of computational complexity is introduced, demonstrating what can and cannot be computed efficiently at scale, helping programmers make informed judgements about the algorithms they use. The easy-to-read text assumes some basic experience in computer programming and familiarity in an object-oriented language, but not necessarily with Python. Topics and features: Includes introductory and advanced data structures and algorithms topics, with suggested chapter sequences for those respective courses Provides learning goals, review questions, and programming exercises in each chapter, as well as numerous examples Presents a primer on Python for those coming from a different language background Adds a new chapter on multiprocessing with Python using the DragonHPC multinode implementation of multiprocessing (includes a tutorial) Reviews the use of hashing in sets and maps, and examines binary search trees, tree traversals, and select graph algorithms Offers downloadable programs and supplementary files at an associated website to help students Students of computer science will find this clear and concise textbook invaluable for undergraduate courses on data structures and algorithms, at both introductory and advanced levels. The book is also suitable as a refresher guide for computer programmers starting new jobs working with Python.