Search Results - "Lin, Chih-Jen"

A note on Platt's probabilistic outputs for support vector machines.

Authors: Lin, Hsuan-Tien Lin, Chih-Jen Wen, Ruby C.

Source: Machine Learning; Oct2007, Vol. 68 Issue 3, p267-276, 10p

A simple decomposition method for support vector machines.

Authors: Hsu, Chih-Wei Lin, Chih-Jen

Source: Machine Learning; January/February/March 2002, Vol. 46, p291-314, 24p

The Effect of an Er, Cr: YSGG Laser Combined with Implantoplasty Treatment on Implant Surface Roughness and Morphologic Analysis: A Pilot In Vitro Study.

Authors: Lin, Chih-Jen Tsai, Ming-Hsu Wu, Yu-Ling et al.

Source: Journal of Functional Biomaterials; Sep2022, Vol. 13 Issue 3, pN.PAG-N.PAG, 14p

On the Convergence of Multiplicative Update Algorithms for Nonnegative Matrix Factorization.

Authors: Lin, Chih-Jen

Source: IEEE Transactions on Neural Networks; November 2007, Vol. 18 Issue 6, p1589-1596, 8p

A Study on SMO-Type Decomposition Methods for Support Vector Machines.

Authors: Chen, Pai-Hsuen Fan, Rong-En Lin, Chih-Jen

Source: IEEE Transactions on Neural Networks; July 2006, Vol. 17 Issue 4, p893-908, 16p

Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001.

Authors: Chen, Bo-Juen Chang, Ming-Wei Lin, Chih-Jen

Source: IEEE Transactions on Power Systems; Nov2004, Vol. 19 Issue 4, p1821-1830, 10p

Analysis of Switching Dynamics With Competing Support Vector Machines.

Authors: Chang, Ming-Wei Lin, Chih-Jen Weng, Ruby Chiu-Hsing

Source: IEEE Transactions on Neural Networks; May2004, Vol. 15 Issue 3, p720-727, 8p

A Study on Reduced Support Vector Machines.

Authors: Lin, Kuan-Ming Lin, Chih-Jen

Source: IEEE Transactions on Neural Networks; November 2003, Vol. 14 Issue 6, p1449-1459, 11p

A Formal Analysis of Stopping Criteria of Decomposition Methods for Support Vector Machines.

Authors: Lin, Chih-Jen

Source: IEEE Transactions on Neural Networks; September 2002, Vol. 13 Issue 5, p1045-1052, 8p

A comparison of methods for multiclass support vector machines.

Authors: Hsu, Chih-Wei Lin, Chih-Jen

Source: IEEE Transactions on Neural Networks; March 2002, Vol. 13 Issue 2, p415-425, 11p

On the convergence of the decomposition method for support vector machines.

Authors: Lin, Chih-Jen

Source: IEEE Transactions on Neural Networks; November 2001, Vol. 12 Issue 6, p1288-1298, 11p

Distributed Newton Methods for Deep Neural Networks.

Authors: Wang, Chien-Chih Tan, Kent Loong Chen, Chun-Ting et al.

Source: Neural Computation. Jun2018, Vol. 30 Issue 6, p1673-1724. 52p. 2 Diagrams, 5 Charts, 2 Graphs.

Subjects: Deep learning, Mathematical optimization, Machine learning, Artificial neural networks, Newton-Raphson method

The analysis of decomposition methods for support vector machines.

Authors: Chang, Chih-Chung Hsu, Chih-Wei Lin, Chih-Jen

Source: IEEE Transactions on Neural Networks; July 2000, Vol. 11 Issue 4, p1003-1008, 6p

Built-in self-test for digital integrated circuits.

Authors: Agrawal, Vishwani D. Lin, Chih-Jen Rutkowski, Paul W.

Source: AT&T Technical Journal; March/April 1994, Vol. 73, p30-39, 10p

Incomplete Cholesky factorizations with limited memory.

Authors: Lin, Chih-Jen Moré, Jorge J.

Source: SIAM Journal on Scientific Computing; August/September 1999, Vol. 21 Issue 1, p24-45, 22p

A Study on Truncated Newton Methods for Linear Classification.

Authors: Galli, Leonardo Lin, Chih-Jen

Source: IEEE Transactions on Neural Networks & Learning Systems. Jul2022, Vol. 33 Issue 7, p2828-2841. 14p.

Subjects: Newton-Raphson method, Classification

Improved Derivation Efficiency and Pluripotency of Stem Cells from the Refractory Inbred C57BL/6 Mouse Strain by Small Molecules.

Authors: Lin, Chih-Jen Amano, Tomokazu Tang, Yong et al.

Source: PLoS ONE. Sep2014, Vol. 9 Issue 9, p1-10. 10p.

Asymptotic convergence of an SMO algorithm without any assumptions.

Authors: Lin, Chih-Jen

Source: IEEE Transactions on Neural Networks; January 2002, Vol. 13 Issue 1, p248-250, 3p

This result is not displayed to guests.
Login for full access.

Dual coordinate descent methods for logistic regression and maximum entropy models.

Authors: Yu, Hsiang-Fu Huang, Fang-Lan Lin, Chih-Jen

Source: Machine Learning; Oct2011, Vol. 85 Issue 1-2, p41-75, 35p