-
1
-
-
0016355478
-
A new look at the statistical model identification
-
Akaike H. A new look at the statistical model identification IEEE Transactions on Automatic Control 19 1974 716 723
-
(1974)
IEEE Transactions on Automatic Control
, vol.19
, pp. 716-723
-
-
Akaike, H.1
-
3
-
-
84886567160
-
-
University of California, School of Information and Computer Science Irvine, CA
-
Bache K., and Lichman M. UCI machine learning repository 2013 University of California, School of Information and Computer Science Irvine, CA
-
(2013)
UCI machine learning repository
-
-
Bache, K.1
Lichman, M.2
-
6
-
-
84994196386
-
Mixed-integer second-order cone programming: A survey
-
Topaloglu H. INFORMS Catonsville, MD
-
Benson H.Y., and Saʇram Ü. Mixed-integer second-order cone programming: A survey Topaloglu H. Tutorials in operations research 2013 INFORMS Catonsville, MD 13 36
-
(2013)
Tutorials in operations research
, pp. 13-36
-
-
Benson, H.Y.1
Saʇram, Ü.2
-
9
-
-
0031334221
-
Selection of relevant features and examples in machine learning
-
Blum A.L., and Langley P. Selection of relevant features and examples in machine learning Artificial Intelligence 97 1997 245 271
-
(1997)
Artificial Intelligence
, vol.97
, pp. 245-271
-
-
Blum, A.L.1
Langley, P.2
-
14
-
-
29144477149
-
An efficient support vector machine learning method with second-order cone programming for large-scale problems
-
Debnath R., Muramatsu M., and Takahashi H. An efficient support vector machine learning method with second-order cone programming for large-scale problems Applied Intelligence 23 2005 219 239
-
(2005)
Applied Intelligence
, vol.23
, pp. 219-239
-
-
Debnath, R.1
Muramatsu, M.2
Takahashi, H.3
-
15
-
-
0001878035
-
Multiple regression analysis
-
Ralston A. Wilf H.S. John Wiley & Sons New York, NY
-
Efroymson M.A. Multiple regression analysis Ralston A. Wilf H.S. Mathematical methods for digital computers 1960 John Wiley & Sons New York, NY 191 203
-
(1960)
Mathematical methods for digital computers
, pp. 191-203
-
-
Efroymson, M.A.1
-
17
-
-
0016128505
-
Regressions by leaps and bounds
-
Furnival G.M., and Wilson R.W. Jr. Regressions by leaps and bounds Technometrics 16 1974 499 511
-
(1974)
Technometrics
, vol.16
, pp. 499-511
-
-
Furnival, G.M.1
Wilson, R.W.2
-
21
-
-
0017280570
-
The analysis and selection of variables in linear regression
-
Hocking R.R. The analysis and selection of variables in linear regression Biometrics 32 1976 1 49
-
(1976)
Biometrics
, vol.32
, pp. 1-49
-
-
Hocking, R.R.1
-
22
-
-
84942484786
-
Ridge regression: Biased estimation for non-orthogonal problems
-
Hoerl A.E., and Kennard R.W. Ridge regression: Biased estimation for non-orthogonal problems Technometrics 20 1970 55 67
-
(1970)
Technometrics
, vol.20
, pp. 55-67
-
-
Hoerl, A.E.1
Kennard, R.W.2
-
23
-
-
34548210088
-
Efficient algorithms for computing the best subset regression models for large-scale problems
-
Hofmann M., Gatu G., and Kontoghiorghes E.J. Efficient algorithms for computing the best subset regression models for large-scale problems Computational Statistics & Data Analysis 52 2007 16 29
-
(2007)
Computational Statistics & Data Analysis
, vol.52
, pp. 16-29
-
-
Hofmann, M.1
Gatu, G.2
Kontoghiorghes, E.J.3
-
25
-
-
0031381525
-
Wrappers for feature subset selection
-
Kohavi R., and John G.H. Wrappers for feature subset selection Artificial Intelligence 97 1997 273 324
-
(1997)
Artificial Intelligence
, vol.97
, pp. 273-324
-
-
Kohavi, R.1
John, G.H.2
-
26
-
-
77954952366
-
Multi-step methods for choosing the best set of variables in regression analysis
-
Konno H., and Takaya Y. Multi-step methods for choosing the best set of variables in regression analysis Computational Optimization and Applications 46 2010 417 426
-
(2010)
Computational Optimization and Applications
, vol.46
, pp. 417-426
-
-
Konno, H.1
Takaya, Y.2
-
27
-
-
67349161084
-
Choosing the best set of variables in regression analysis using integer programming
-
Konno H., and Yamamoto R. Choosing the best set of variables in regression analysis using integer programming Journal of Global Optimization 44 2009 272 282
-
(2009)
Journal of Global Optimization
, vol.44
, pp. 272-282
-
-
Konno, H.1
Yamamoto, R.2
-
32
-
-
31144448615
-
Using simulated annealing to optimize the feature selection problem in marketing applications
-
Meiri R., and Zahavi J. Using simulated annealing to optimize the feature selection problem in marketing applications European Journal of Operational Research 171 2006 842 858
-
(2006)
European Journal of Operational Research
, vol.171
, pp. 842-858
-
-
Meiri, R.1
Zahavi, J.2
-
35
-
-
84940589205
-
Feature subset selection for logistic regression via mixed integer optimization
-
Department of Policy and Planning Sciences, University of Tsukuba
-
Sato T., Takano Y., Miyashiro R., and Yoshise A. Feature subset selection for logistic regression via mixed integer optimization Discussion Paper Series, No. 1324 2015 Department of Policy and Planning Sciences, University of Tsukuba
-
(2015)
Discussion Paper Series, No. 1324
-
-
Sato, T.1
Takano, Y.2
Miyashiro, R.3
Yoshise, A.4
-
36
-
-
0000120766
-
Estimating the dimension of a model
-
Schwarz G. Estimating the dimension of a model Annals of Statistics 6 1978 461 464
-
(1978)
Annals of Statistics
, vol.6
, pp. 461-464
-
-
Schwarz, G.1
-
39
-
-
0032215931
-
Comparison of certain MINLP algorithms when applied to a model structure determination and parameter estimation problem
-
Skrifvars H., Leyffer S., and Westerlund T. Comparison of certain MINLP algorithms when applied to a model structure determination and parameter estimation problem Computers & Chemical Engineering 22 1998 1829 1835
-
(1998)
Computers & Chemical Engineering
, vol.22
, pp. 1829-1835
-
-
Skrifvars, H.1
Leyffer, S.2
Westerlund, T.3
-
40
-
-
84963178774
-
Further analysts of the data by Akaike's information criterion and the finite corrections
-
Sugiura N. Further analysts of the data by Akaike's information criterion and the finite corrections Communications in Statistics - Theory and Methods 7 1978 13 26
-
(1978)
Communications in Statistics - Theory and Methods
, vol.7
, pp. 13-26
-
-
Sugiura, N.1
-
43
-
-
0000536634
-
A new formula for predicting the shrinkage of the coefficient of multiple correlation
-
Wherry R.J. A new formula for predicting the shrinkage of the coefficient of multiple correlation The Annals of Mathematical Statistics 2 1931 440 457
-
(1931)
The Annals of Mathematical Statistics
, vol.2
, pp. 440-457
-
-
Wherry, R.J.1
|