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.
Cluster analysis Grouping a set of objects by similarity 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 specific sense defined by the analyst) to each other than to those in other groups (clusters). It is a main task of exploratory data analysis, and a common technique for statistical data analysis, used in many fields, including pattern recognition, image analysis, information retrieval, bioinformatics, data compression, computer graphics and machine learning. Besides the term clustering, there is a number of terms with similar meanings, including automatic classification, numerical taxonomy, botryology (from Greek βότρυς "grape"), typological analysis, and community detection. Definition[edit] The notion of a "cluster" cannot be precisely defined, which is one of the reasons why there are so many clustering algorithms.[5] There is a common denominator: a group of data objects. to centroid .
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]
Music Tech Fest 2013: the Festival of Music Ideas Invalid quantity. Please enter a quantity of 1 or more. The quantity you chose exceeds the quantity available. Please enter your name. Please enter an email address. Please enter a valid email address. Please enter your message or comments. Please enter the code as shown on the image. Please select the date you would like to attend. Please enter a valid email address in the To: field. Please enter a subject for your message. Please enter a message. You can only send this invitations to 10 email addresses at a time. $$$$ is not a properly formatted colour. Please limit your message to $$$$ characters. $$$$ is not a valid email address. Please enter a promotional code. Sold Out Pending You have exceeded the time limit and your reservation has been released. The purpose of this time limit is to ensure that registration is available to as many people as possible. This option is not available anymore. Please read and accept the waiver. All fields marked with * are required. US Zipcodes need to be 5 digits. Map
Data mining Process of extracting and discovering patterns in large data sets Data mining is the process of extracting and 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 of extracting information (with intelligent methods) from a data set and transforming 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] Background[edit] The manual extraction of patterns from data has occurred for centuries. Process[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. Filtatron DBSCAN Density-based data clustering algorithm 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 non-parametric 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, and most commonly cited, clustering algorithms.[2] The popular follow-up HDBSCAN* was initially published by Ricardo J. History[edit] In 1972, Robert F. 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 (including p).A point q is directly reachable from p if point q is within distance ε from core point p. Algorithm[edit] where . Notes[edit]