Weka (apprentissage automatique)
Un article de Wikipédia, l'encyclopédie libre. Pour les articles homonymes, voir Weka. Le logo Weka. Le weka est un oiseau endémique de la Nouvelle-Zélande. Weka supporte plusieurs outils d'exploration de données standards, et en particulier, des préprocesseurs de données, des agrégateurs de données (data clustering), des classificateurs statistiques, des analyseurs de régression, des outils de visualisation, et des outils d'analyse discriminante. Toutes les techniques de Weka reposent sur la supposition que les données sont disponibles dans un unique fichier plat ou une Relation binaire, ou chaque type de donnée est décrit par un nombre fixe d'attributs (les attributs ordinaires, numériques ou symboliques, mais quelques autres types d'attributs sont aussi supportés). L'interface principale de Weka est l’Explorer, mais à peu près les mêmes fonctionnalités peuvent être atteintes via l'interface Flux de Connaissance de chaque composant et depuis la ligne de commande.
First Steps | anonymox.net
You have installed anonymoX successfully, and your are already anonymous on the internet. anonymoX is not only an Add-On. We provide an anonymization network to anonymize your internet traffic (websites, videos, downloads) within Firefox. Please note: anonymoX will make your Internet slower This is because you use anonymization servers of our free-to-use anonymization network. For high-speed anonymization see our Premium package: To switch between the different provided virtual Identities, use the anonymoX-Icon (blue X), located on the right-hand side of the search bar. IP-Address , and optionally clear cookies Profiles If you don't want to use anonymoX for certain Websites on which a faked Identity is useless, like Online-Banking, or just want to look like originating from another country for a specific website: When you are on that Website, enter the anonymoX menu by clicking on the blue X, select the Name of the Website at the topmost menu ( "Settings for"). The way anonymization works
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]
Cours Data Mining
Contenu et objectifs du cours DATA MINING - DATA SCIENCE Data Mining Le DATA MINING , raccourci de "Extraction de Connaissances à partir de Données" ("Knowledge Discovery in Databases" en anglais - KDD), est un domaine très en vogue. A la lecture des différents documents essayant tant bien que mal de définir exactement ce qu'est le data mining, on peut se dire que, finalement, cela fait plus de 30 ans qu'on le pratique avec ce qu'on appelle l'analyse de données et les statistiques exploratoires. Et on n'aurait pas complètement tort. En réalité, ce n'est pas aussi simple, le data mining emmène plusieurs points nouveaux qui sont loin d'être négligeables : (1) des techniques d'analyse qui ne sont pas dans la culture des statisticiens, en provenance de l'apprentissage automatique (Intelligence artificielle), de la reconnaissance de formes (pattern recognition) et des bases de données ; (2) l'extraction de connaissances est intégrée dans le schéma organisationnel de l'entreprise. Public visé
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Manage Your Data: Data Management: Subject Guides
The MIT Libraries supports the MIT community in the management and curation of research data by providing the following services: Data Management Guide This Data Management and Publishing Guide is a practical self-help guide to the management and curation of research data throughout its life cycle. It provides guidance on a range of topics, including: planning for data management, documentation/metadata, file formats, data organization, data security and backup, citing data, data integration, funder requirements, ethical and legal issues, and sharing and archiving data. Assistance with Creating Data Management Plans Many funders, such as the National Science Foundation, have requirements for data sharing and data management plans. Workshops Our workshops teach you how to manage data more efficiently for your own use and help you to effectively share your data with others. Individual Consultation and Collaboration with Researchers Referrals to Related Services Contact Us
Future of game-based Learning - Discussions, ideas & thoughts on the future of game-based learning
Metadata Extraction Tool - Introduction
Serious Games & Jeux Sérieux
Standard Naming Conventions For Electronic Records: The Rules/Anne Thompson
Just two weeks ago, I had the opportunity to attend the 2012 Annual Meeting of the Society of American Archivists. Sporting the theme “Beyond Borders,” I was impressed by the recent transformation in how archives and archivists “do business”—how the technological and digital border has for the most part disappeared. Five years ago, the handful of conference sessions talking about digital records focused on how to capture and preserve born-digital records. This year, most sessions touched on digitization and digital records not as a novelty topic, but as one of today’s facts of life. An 1888 image of the Lucius D. Then and now on my phone. Wikipedians and Archives staff collaborate in the edit-a-thon war room at the She Blinded Me With Science edit-a-thon. Going where the people go. Tennessee v. Relevant connections. The New York Public Library's What's On the Menu? Conversations enrich collections. In :
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