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Machine learning

Machine learning
Machine learning is a subfield of computer science[1] that evolved from the study of pattern recognition and computational learning theory in artificial intelligence.[1] Machine learning explores the construction and study of algorithms that can learn from and make predictions on data.[2] Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions,[3]:2 rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making. It has strong ties to mathematical optimization, which deliver methods, theory and application domains to the field. Machine learning is employed in a range of computing tasks where designing and programming explicit, rule-based algorithms is infeasible. Overview[edit] Tom M. Types of problems and tasks[edit] History and relationships to other fields[edit] Theory[edit]

Artificial Intelligence and Machine Learning A Gaussian Mixture Model Layer Jointly Optimized with Discriminative Features within A Deep Neural Network Architecture Ehsan Variani, Erik McDermott, Georg Heigold ICASSP, IEEE (2015) Adaptation algorithm and theory based on generalized discrepancy Corinna Cortes, Mehryar Mohri, Andrés Muñoz Medina Proceedings of the 21st ACM Conference on Knowledge Discovery and Data Mining (KDD 2015) Adding Third-Party Authentication to Open edX: A Case Study John Cox, Pavel Simakov Proceedings of the Second (2015) ACM Conference on Learning @ Scale, ACM, New York, NY, USA, pp. 277-280 An Exploration of Parameter Redundancy in Deep Networks with Circulant Projections Yu Cheng, Felix X.

Gestion des connaissances Un article de Wikipédia, l'encyclopédie libre. La gestion des connaissances (en anglais knowledge management) est une démarche managériale pluridisciplinaire qui regroupe l'ensemble des initiatives, des méthodes et des techniques permettant de percevoir, identifier, analyser, organiser, mémoriser, partager les connaissances des membres d'une organisation – les savoirs créés par l'entreprise elle-même (marketing, recherche et développement) ou acquis de l'extérieur (intelligence économique) – en vue d'atteindre un objectif fixé. Définition[modifier | modifier le code] Actuellement, nous sommes submergés d'informations. La Gestion des Connaissances est une démarche stratégique pluridisciplinaire visant à atteindre l'objectif fixé grâce à une exploitation optimale des connaissances.[1] D'après des praticiens et des académiciens tels que R. Historique[modifier | modifier le code] Enjeux et objectifs[modifier | modifier le code] Les formes des connaissances[modifier | modifier le code] SI MARIÉ(?

Conservative Myths and the Death of Marlboro Man Those of a certain age remember TV ads featuring the Marlboro Man – a rugged individual who rode a horse through an America that even then had long since disappeared. He was self-reliant. No moocher. He inhabited Marlboro Country. The Marlboro Man is dead. Of course, the whole thing was an extraordinarily destructive myth – just like the myths the Republican’s have been selling for 30 years, now. This is the bull being marketed by Republicans to the American people. This is why they’re threatening to take us over the fiscal cliff. Let’s say that again, in words that even Fox News and the Wall Street Journal can understand. How about austerity? Austerity budgets are – and always have been – about disabling government. This economic philosophy will bring about the same end as that faced by the Marlboro Men. But it’s not just the fiscal cliff and faux austerity. Climate Change? You’d think this wouldn’t be too much to ask. But you would be wrong. We are at a crossroads.

Internet U.S. Army soldiers "surfing the Internet" at Forward Operating Base Yusifiyah, Iraq The Internet is a global system of interconnected computer networks that use the standard Internet protocol suite (TCP/IP) to link several billion devices worldwide. It is a network of networks[1] that consists of millions of private, public, academic, business, and government networks of local to global scope, linked by a broad array of electronic, wireless, and optical networking technologies. The Internet carries an extensive range of information resources and services, such as the inter-linked hypertext documents and applications of the World Wide Web (WWW), the infrastructure to support email, and peer-to-peer networks for file sharing and telephony. Most traditional communications media, including telephony and television, are being reshaped or redefined by the Internet, giving birth to new services such as voice over Internet Protocol (VoIP) and Internet Protocol television (IPTV). Terminology Users

Cluster analysis Task of grouping a set of objects so that objects in the same group (or cluster) are more similar to each other than to those in other clusters The result of a cluster analysis shown as the coloring of the squares into three clusters. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). Besides the term clustering, there are a number of terms with similar meanings, including automatic classification, numerical taxonomy, botryology (from Greek βότρυς "grape"), typological analysis, and community detection. Cluster analysis was originated in anthropology by Driver and Kroeber in 1932[1] and introduced to psychology by Joseph Zubin in 1938[2] and Robert Tryon in 1939[3] and famously used by Cattell beginning in 1943[4] for trait theory classification in personality psychology. Definition[edit] Algorithms[edit] to centroid and

Gaussian Processes for Machine Learning: Contents Carl Edward Rasmussen and Christopher K. I. Williams MIT Press, 2006. ISBN-10 0-262-18253-X, ISBN-13 978-0-262-18253-9. This book is © Copyright 2006 by Massachusetts Institute of Technology. The MIT Press have kindly agreed to allow us to make the book available on the web. The whole book as a single pdf file. List of contents and individual chapters in pdf format Frontmatter Table of Contents Series Foreword Preface Symbols and Notation 1 Introduction 1.1 A Pictorial Introduction to Bayesian Modelling 1.2 Roadmap 2 Regression 2.1 Weight-space View 2.2 Function-space View 2.3 Varying the Hyperparameters 2.4 Decision Theory for Regression 2.5 An Example Application 2.6 Smoothing, Weight Functions and Equivalent Kernels 2.7 History and Related Work 2.8 Appendix: Infinite Radial Basis Function Networks 2.9 Exercises 3 Classification 3.1 Classification Problems 3.2 Linear Models for Classification 3.3 Gaussian Process Classification 3.4 The Laplace Approximation for the Binary GP Classifier 3.7 Experiments

Genetic algorithm The 2006 NASA ST5 spacecraft antenna. This complicated shape was found by an evolutionary computer design program to create the best radiation pattern. Genetic algorithms find application in bioinformatics, phylogenetics, computational science, engineering, economics, chemistry, manufacturing, mathematics, physics, pharmacometrics and other fields. Methodology[edit] In a genetic algorithm, a population of candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem is evolved toward better solutions. A typical genetic algorithm requires: a genetic representation of the solution domain,a fitness function to evaluate the solution domain. Once the genetic representation and the fitness function are defined, a GA proceeds to initialize a population of solutions and then to improve it through repetitive application of the mutation, crossover, inversion and selection operators. Initialization of genetic algorithm[edit] Selection[edit] Genetic operators[edit]

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Data mining Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.[1] Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for further use.[1][2][3][4] Data mining is the analysis step of the "knowledge discovery in databases" process or KDD.[5] Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.[1] Etymology[edit] In the 1960s, statisticians and economists used terms like data fishing or data dredging to refer to what they considered the bad practice of analyzing data without an a-priori hypothesis.

DBSCAN Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm proposed by Martin Ester, Hans-Peter Kriegel, Jörg Sander and Xiaowei Xu in 1996.[1] It is a density-based clustering algorithm: given a set of points in some space, it groups together points that are closely packed together (points with many nearby neighbors), marking as outliers points that lie alone in low-density regions (whose nearest neighbors are too far away). DBSCAN is one of the most common clustering algorithms and also most cited in scientific literature.[2] In 2014, the algorithm was awarded the test of time award (an award given to algorithms which have received substantial attention in theory and practice) at the leading data mining conference, KDD.[3] Preliminary[edit] Consider a set of points in some space to be clustered. A point p is a core point if at least minPts points are within distance ε of it, and those points are said to be directly reachable from p. Notes[edit]

Machine Learning Seven Aspects of Strategy Formation Exploring the Value of Planning Abstract It has been widely argued that the planning approach that dominates entrepreneurial training does not represent either actual or good strategic decision-making.

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