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» Predicting Time Series with Support Vector Machines
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CORR
2010
Springer
149views Education» more  CORR 2010»
15 years 3 months ago
Using Rough Set and Support Vector Machine for Network Intrusion Detection
The main function of IDS (Intrusion Detection System) is to protect the system, analyze and predict the behaviors of users. Then these behaviors will be considered an attack or a ...
Rung Ching Chen, Kai-Fan Cheng, Chia-Fen Hsieh
146
Voted
KAIS
2010
144views more  KAIS 2010»
15 years 1 months ago
Boosting support vector machines for imbalanced data sets
Real world data mining applications must address the issue of learning from imbalanced data sets. The problem occurs when the number of instances in one class greatly outnumbers t...
Benjamin X. Wang, Nathalie Japkowicz
JCDL
2003
ACM
160views Education» more  JCDL 2003»
15 years 8 months ago
Automatic Document Metadata Extraction Using Support Vector Machines
Automatic metadata generation provides scalability and usability for digital libraries and their collections. Machine learning methods offer robust and adaptable automatic metadat...
Hui Han, C. Lee Giles, Eren Manavoglu, Hongyuan Zh...
222
Voted
ICML
2010
IEEE
15 years 4 months ago
Dynamical Products of Experts for Modeling Financial Time Series
Predicting the "Value at Risk" of a portfolio of stocks is of great significance in quantitative finance. We introduce a new class models, "dynamical products of ex...
Yutian Chen, Max Welling
115
Voted
ECAI
2004
Springer
15 years 8 months ago
A Generalized Quadratic Loss for Support Vector Machines
The standard SVM formulation for binary classification is based on the Hinge loss function, where errors are considered not correlated. Due to this, local information in the featu...
Filippo Portera, Alessandro Sperduti