Title :

Learning from Networked Examples

Author(s) :

Wang, Yuyi [Auteur]
Nanjing Institute of Geology and Palaeontology [NIGPAS-CAS]
Guo, Zheng-Chu [Auteur]
Zhejiang University [Hangzhou, China]
Ramon, Jan [Auteur]
Machine Learning in Information Networks [MAGNET]

Scientific editor(s) :

Steve Hanneke
Lev Reyzin

Conference title :

ALT 2017 - 28th conference on Algorithmic Learning Theory

City :

Kyoto

Country :

Japon

Start date of the conference :

2017-10-15

Publication date :

2017

English keyword(s) :

Non-iid Sample
Networked Example
Concentration Inequality
Sample Error Bound
Generalization Error Bound

HAL domain(s) :

Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Théorie de l'information [cs.IT]
Mathématiques [math]/Statistiques [math.ST]
Physique [physics]/Physique [physics]/Analyse de données, Statistiques et Probabilités [physics.data-an]
Statistiques [stat]/Machine Learning [stat.ML]

English abstract : [en]

Many machine learning algorithms are based on the assumption that training examples are drawn independently. However, this assumption does not hold anymore when learning from a networked sample because two or more training ...
Show more >Many machine learning algorithms are based on the assumption that training examples are drawn independently. However, this assumption does not hold anymore when learning from a networked sample because two or more training examples may share some common objects, and hence share the features of these shared objects. We show that the classic approach of ignoring this problem potentially can have a harmful effect on the accuracy of statistics, and then consider alternatives. One of these is to only use independent examples, discarding other information. However, this is clearly suboptimal. We analyze sample error bounds in this networked setting, providing significantly improved results. An important component of our approach is formed by efficient sample weighting schemes, which leads to novel concentration inequalities.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL