Nonlinear Methods for Communications

1992 ◽  
Author(s):  
LORAL WESTERN DEVELOPMENT LABS SAN JOSE CA
Keyword(s):  
Nonlinear Methods

Nonlinear methods of measuring picosecond pulses

1984 ◽  
Vol 27 (1) ◽  
pp. 88-91
Author(s):  
Yu. V. Vvedenskii ◽  
A. B. Zuev
Keyword(s):  
Nonlinear Methods ◽  
Picosecond Pulses

scholarly journals Large-Scale Linear RankSVM

2014 ◽  
Vol 26 (4) ◽  
pp. 781-817 ◽  
Author(s):  
Ching-Pei Lee ◽  
Chih-Jen Lin
Keyword(s):  
Decision Trees ◽  
Computational Efficiency ◽  
Efficient Algorithm ◽  
Large Scale ◽  
Learning To Rank ◽  
Gradient Boosting ◽  
Baseline Model ◽  
Nonlinear Methods ◽  
Advantages And Disadvantages ◽  
Linear Ranksvm

Linear rankSVM is one of the widely used methods for learning to rank. Although its performance may be inferior to nonlinear methods such as kernel rankSVM and gradient boosting decision trees, linear rankSVM is useful to quickly produce a baseline model. Furthermore, following its recent development for classification, linear rankSVM may give competitive performance for large and sparse data. A great deal of works have studied linear rankSVM. The focus is on the computational efficiency when the number of preference pairs is large. In this letter, we systematically study existing works, discuss their advantages and disadvantages, and propose an efficient algorithm. We discuss different implementation issues and extensions with detailed experiments. Finally, we develop a robust linear rankSVM tool for public use.

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