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POO : TYPES OBJET
Cette classe crée le type objet PersonneV1.Les deux lignes qui commencent par private indiquent que chaque objet de type PersonneV1 contiendra deux variables, firstName et lastName, toutes deux de type String.Les informations associées à un objet de type PersonneV1 seront donc deux chaînes de caractères.. Pour utiliser le type ainsi défini, on procède comme pour les autres types objets AN INTRODUCTION TO STATISTICAL LEARNING THEORY An introduction to statistical learning theory Fabrice Rossi TELECOM ParisTech November 2008POO : TABLEAUX
FABRICE ROSSITRANSLATE THIS PAGE Fabrice Rossi. Je suis professeur de science des données l'Université Paris-Dauphine et membre du CEREMADE. Jusqu'en août 2019, j'étais professeur à l'Université Paris 1 Panthéon-Sorbonne et membre de l'équipe de recherche SAMM.. La partie française de mon site est presque entièrement dédiée à mon activité d'enseignement, ainsi qu'à quelques sujets personnels qui pourraient POO : HÉRITAGETRANSLATE THIS PAGESEE MORE ON APIACOA.ORGINITIATION À RPART
TP R : MOD ELE DE M ELANGE ET ALGORITHME EM TP R : mod ele de m elange et algorithme EM Fabrice Rossi 1 Rappels 1.1 Mod ele de m elange On se donne Nobservations dans Rd, les (x i) 1 i N, qu’on suppose des r SYSTÈMES RÉPARTIS : CORBA Plan 1. Architecture et modèle objet 2. Interface Definition Language 3. Accès à l’ORB 4. Naming Service 5. Processus de développement Syst`emes r´epartis : CORBA – p.2/45 LE MODÈLE LINÉAIRE AVEC R Le mod`ele lin´eaire avec R Fabrice Rossi 31 mars 2003 1 Le mod`ele lin´eaire simple 1.1 Introduction Le logiciel R propose une fonction lm qui permet d’estimer les param`etres d’un mod`ele lin´eaire. GRAPH CLUSTERING AND GRAPH VISUALIZATIONSEE MORE ON APIACOA.ORGPOO : TYPES OBJET
Cette classe crée le type objet PersonneV1.Les deux lignes qui commencent par private indiquent que chaque objet de type PersonneV1 contiendra deux variables, firstName et lastName, toutes deux de type String.Les informations associées à un objet de type PersonneV1 seront donc deux chaînes de caractères.. Pour utiliser le type ainsi défini, on procède comme pour les autres types objets AN INTRODUCTION TO STATISTICAL LEARNING THEORY An introduction to statistical learning theory Fabrice Rossi TELECOM ParisTech November 2008POO : TABLEAUX
FABRICE ROSSITRANSLATE THIS PAGE Fabrice Rossi. Je suis professeur de science des données l'Université Paris-Dauphine et membre du CEREMADE. Jusqu'en août 2019, j'étais professeur à l'Université Paris 1 Panthéon-Sorbonne et membre de l'équipe de recherche SAMM.. La partie française de mon site est presque entièrement dédiée à mon activité d'enseignement, ainsi qu'à quelques sujets personnels qui pourraient POO : HÉRITAGETRANSLATE THIS PAGESEE MORE ON APIACOA.ORGINITIATION À RPART
TP R : MOD ELE DE M ELANGE ET ALGORITHME EM TP R : mod ele de m elange et algorithme EM Fabrice Rossi 1 Rappels 1.1 Mod ele de m elange On se donne Nobservations dans Rd, les (x i) 1 i N, qu’on suppose des r SYSTÈMES RÉPARTIS : CORBA Plan 1. Architecture et modèle objet 2. Interface Definition Language 3. Accès à l’ORB 4. Naming Service 5. Processus de développement Syst`emes r´epartis : CORBA – p.2/45 LE MODÈLE LINÉAIRE AVEC R Le mod`ele lin´eaire avec R Fabrice Rossi 31 mars 2003 1 Le mod`ele lin´eaire simple 1.1 Introduction Le logiciel R propose une fonction lm qui permet d’estimer les param`etres d’un mod`ele lin´eaire. AN INTRODUCTION TO DATABASE MANAGEMENT SYSTEM Example id first_name last_name gender film_count 933 Lewis Abernathy M 1 2547 Andrew Adamson M 1 2700 William Addy M 1 2898 Seth(I) Adkins M 1
MEAN ABSOLUTE PERCENTAGE ERROR FOR REGRESSION MODELS 75 As pointed out above, this shows that, when P(T= 0) = 0, J(m) is nite if and only if both E I T2]0;1] T and E I T2[ 1;0[T are nite. We obtain slightly more operational conditions in the rest of the proof. Let us therefore introduce the following functions: OPTIMIZING AN ORGANIZED MODULARITY MEASURE FOR TOPOGRAPHIC Optimizing an Organized Modularity Measure for Topographic Graph Clustering: a Deterministic Annealing Approach Fabrice Rossia, Nathalie Villa-Vialaneixb,c aBILab, T el ecom ParisTech, LTCI - UMR CNRS 5141, France bInstitut de Math ematiques de Toulouse, Universit e de Toulouse, France cIUT STID (Carcassonne), Universit e de PerpignanVia Domitia, France
THE RELATIONAL MODEL Relational model History I invented by Edgar F. Codd in 1969-1970 I based on a mathematical model, the relational algebra I associatedrelational calculus
A TRICLUSTERING APPROACH FOR TIME EVOLVING GRAPHS A Triclustering Approach for Time Evolving Graphs Romain Guigour`es y, Marc Boulle´, Fabrice Rossiy Orange Labs, Lannion, France ySAMM EA 4543, Universit´e Paris 1, Paris, France Email: romain.guigoures@orange.com, marc.boulle@orange.com, fabrice.rossi@univ-paris1.fr DISCOVERING PATTERNS IN TIME-VARYING GRAPHS: A 2 R. Guigour es et al. 1 Introduction In real world problems, interactions between entities are generally evolving through time. This is the case for instance in transportation networks (roads,train, etc)
GRAPH-BASED APPROACHES TO CLUSTERING NETWORK-CONSTRAINED Graph-Based Approaches to Clustering Network-Constrained Trajectory Data Mohamed K. El Mahrsi1 and Fabrice Rossi2 1 T el ecom ParisTech, D epartement INFRES 46, rue Barrault 75634 Paris CEDEX 13, France khalil.mahrsi@telecom-paristech.fr HIERARCHICAL CLUSTERING FOR GRAPH VISUALIZATION Hierarchical clustering for graph visualization St ephan Cl emen˘con 1, Hector De Arazoza2, Fabrice Rossi and Viet-Chi Tran3 1- Institut T el ecom, T el ecom ParisTech, LTCI - UMR CNRS 5141 46, rue Barrault, 75013 Paris { France MATRICES -- DARKOVER Matrices Introduction Matrices are blue crystals which have the peculiar power of harmonizing with the human mind to amplify psychic abilities. They are also called star-stones.. They allow Marion Zimmer Bradley to offer a partial explanation for the existence of laran among Darkovans. Physical Description DARKOVER CHRONOLOGY -- DARKOVER Darkover Chronology Problems It is nearly impossible to obtain a coherent history of Darkover. In fact, the novels are full of contradictions and it is therefore difficult toApiacoa.org
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FABRICE ROSSI
I'm professor of data science at Université Paris-Dauphine . I'm a member of the CEREMADE.
The English part of this website is mostly dedicated to my research activities and to various more personal subjects than might be of some interest to others. As I teach almost exclusively in French, teaching related materials are available from the French version ofthis website.
LATEST NEWS
*
2 September 2019 » Cours d'introduction au business analytics*
13 November 2017 » Cours d'introduction à la fouille de graphes*
3 November 2017 » Big Data Course*
before 3 November 2017 » ArchivesLATEST POSTS
*
28 November 2018 » Aichetou Bouchareb's PhD Defense*
1 March 2018 » Welcome to Elie Mengin*
17 November 2017 » Marco Corneli's PhD Defense*
before 17 November 2017 » ArchivesRESEARCH
I'm interested in exploratory data analysis (including visualization), data mining and machine learning. I focus most of my research activities on non standard (non vector) data, ranging from functional data to graphs, as well as on generic dissimilarity based techniques, using kernel machines (such as support vector machines), artificial neural networks, and, more recently, generative models. On an application point of view, I've worked on spectrometric data and on web usage data, but I tend to specialize now on data coming from the humanities (History mainly). Almost all my publications are available on line, together with softwareand data sets
.
TEACHING
As pointed out above, a large part of my teaching activity in computer science and mathematics is done in French with lecture notes written in French. The English ones are available through this page.
CONTACT
You'll find here detailed contact information.*
Fabrice Rossi 2003-2019 Last update: 11 October 2019Credits
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