메뉴 건너뛰기




Volumn , Issue , 2013, Pages

Compressive feature learning

Author keywords

[No Author keywords available]

Indexed keywords

TEXT PROCESSING;

EID: 84898999058     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (16)

References (29)
  • 1
    • 0037185399 scopus 로고    scopus 로고
    • Language trees and zipping
    • D. Benedetto, E. Caglioti, and V. Loreto. Language trees and zipping. PRL, 88(4):048702, 2002.
    • (2002) PRL , vol.88 , Issue.4 , pp. 048702
    • Benedetto, D.1    Caglioti, E.2    Loreto, V.3
  • 2
    • 80051762104 scopus 로고    scopus 로고
    • Distributed optimization and statistical learning via the alternating direction method of multipliers
    • S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine Learning, 3(1):1-122, 2011.
    • (2011) Foundations and Trends in Machine Learning , vol.3 , Issue.1 , pp. 1-122
    • Boyd, S.1    Parikh, N.2    Chu, E.3    Peleato, B.4    Eckstein, J.5
  • 3
    • 33845807843 scopus 로고    scopus 로고
    • Spam filtering using statistical data compression models
    • A. Bratko, B. Filipič, G. V. Cormack, T. R. Lynam, and B. Zupan. Spam filtering using statistical data compression models. JMLR, 7:2673-2698, 2006.
    • (2006) JMLR , vol.7 , pp. 2673-2698
    • Bratko, A.1    Filipič, B.2    Cormack, G.V.3    Lynam, T.R.4    Zupan, B.5
  • 4
    • 0021479943 scopus 로고
    • An O(n) algorithm for quadratic knapsack problems
    • P. Brucker. An O(n) algorithm for quadratic knapsack problems. Operations Research Letters, 3(3):163-166, 1984.
    • (1984) Operations Research Letters , vol.3 , Issue.3 , pp. 163-166
    • Brucker, P.1
  • 5
  • 6
    • 17744364120 scopus 로고    scopus 로고
    • Clustering by compression
    • R. Cilibrasi and P. M. Vitányi. Clustering by compression. TIT, 51(4):1523-1545, 2005.
    • (2005) TIT , vol.51 , Issue.4 , pp. 1523-1545
    • Cilibrasi, R.1    Vitányi, P.M.2
  • 8
    • 77950537175 scopus 로고    scopus 로고
    • Regularization paths for generalized linear models via coordinate descent
    • J. Friedman, T. Hastie, and R. Tibshirani. Regularization paths for generalized linear models via coordinate descent. J Stat Softw, 33(1):1-22, 2010.
    • (2010) J Stat Softw , vol.33 , Issue.1 , pp. 1-22
    • Friedman, J.1    Hastie, T.2    Tibshirani, R.3
  • 9
    • 14344259210 scopus 로고    scopus 로고
    • Text categorization with many redundant features: Using aggressive feature selection to make svms competitive with c4.5
    • E. Gabrilovich and S. Markovitch. Text categorization with many redundant features: Using aggressive feature selection to make SVMs competitive with C4.5. In ICML, 2004.
    • (2004) ICML
    • Gabrilovich, E.1    Markovitch, S.2
  • 10
    • 33745561205 scopus 로고    scopus 로고
    • An introduction to variable and feature selection
    • I. Guyon and A. Elisseeff. An introduction to variable and feature selection. JMLR, 3:1157-1182, 2003.
    • (2003) JMLR , vol.3 , pp. 1157-1182
    • Guyon, I.1    Elisseeff, A.2
  • 12
    • 79251469431 scopus 로고    scopus 로고
    • Tackling box-constrained optimization via a new projected quasi-newton approach
    • D. Kim, S. Sra, and I. S. Dhillon. Tackling box-constrained optimization via a new projected quasi-newton approach. SIAM Journal on Scientific Computing, 32(6):3548-3563, 2010.
    • (2010) SIAM Journal on Scientific Computing , vol.32 , Issue.6 , pp. 3548-3563
    • Kim, D.1    Sra, S.2    Dhillon, I.S.3
  • 13
    • 84897541528 scopus 로고    scopus 로고
    • Fast algorithms for sparse principal component analysis based on Rayleigh quotient iteration
    • V. Kuleshov. Fast algorithms for sparse principal component analysis based on Rayleigh quotient iteration. In ICML, 2013.
    • (2013) ICML
    • Kuleshov, V.1
  • 14
    • 46249120595 scopus 로고    scopus 로고
    • Proposing a new term weighting scheme for text categorization
    • M. Lan, C. Tan, and H. Low. Proposing a new term weighting scheme for text categorization. In AAAI, 2006.
    • (2006) AAAI
    • Lan, M.1    Tan, C.2    Low, H.3
  • 15
    • 85142688646 scopus 로고
    • Newsweeder: Learning to filter netnews
    • K. Lang. Newsweeder: Learning to filter netnews. In ICML, 1995.
    • (1995) ICML
    • Lang, K.1
  • 16
    • 56449110012 scopus 로고    scopus 로고
    • Classification using discriminative restricted Boltzmann machines
    • H. Larochelle and Y. Bengio. Classification using discriminative restricted Boltzmann machines. In ICML, 2008.
    • (2008) ICML
    • Larochelle, H.1    Bengio, Y.2
  • 17
    • 84899005495 scopus 로고    scopus 로고
    • Improving multiclass text classification with error-correcting output coding and sub-class partitions
    • B. Li and C. Vogel. Improving multiclass text classification with error-correcting output coding and sub-class partitions. In Can Conf Adv Art Int, 2010.
    • (2010) Can Conf Adv Art Int
    • Li, B.1    Vogel, C.2
  • 18
    • 17044405923 scopus 로고    scopus 로고
    • Toward integrating feature selection algorithms for classification and clustering
    • H. Liu and L. Yu. Toward integrating feature selection algorithms for classification and clustering. TKDE, 17(4):491-502, 2005.
    • (2005) TKDE , vol.17 , Issue.4 , pp. 491-502
    • Liu, H.1    Yu, L.2
  • 19
    • 1942516906 scopus 로고    scopus 로고
    • An evaluation on feature selection for text clustering
    • T. Liu, S. Liu, Z. Chen, andW. Ma. An evaluation on feature selection for text clustering. In ICML, 2003.
    • (2003) ICML
    • Liu, T.1    Liu, S.2    Chen, Z.3    Ma, W.4
  • 22
    • 84868154922 scopus 로고    scopus 로고
    • accessed May 31, 2013
    • J. Rennie. 20 Newsgroups dataset, 2008. http://qwone.com/~jason/ 20Newsgroups (accessed May 31, 2013).
    • (2008) 20 Newsgroups Dataset
    • Rennie, J.1
  • 23
    • 0018015137 scopus 로고
    • Modeling by shortest data description
    • J. Rissanen. Modeling by shortest data description. Automatica, 14(5):465-471, 1978.
    • (1978) Automatica , vol.14 , Issue.5 , pp. 465-471
    • Rissanen, J.1
  • 24
    • 65749098156 scopus 로고    scopus 로고
    • Compression and machine learning: A new perspective on feature space vectors
    • D. Sculley and C. E. Brodley. Compression and machine learning: A new perspective on feature space vectors. In DCC, 2006.
    • (2006) DCC
    • Sculley, D.1    Brodley, C.E.2
  • 25
    • 85194972808 scopus 로고    scopus 로고
    • Regression shrinkage and selection via the lasso
    • R. Tibshirani. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B, 58(1):267-288, 1996.
    • (1996) J. R. Stat. Soc. B , vol.58 , Issue.1 , pp. 267-288
    • Tibshirani, R.1
  • 26
    • 0003141935 scopus 로고    scopus 로고
    • A comparative study on feature selection in text categorization
    • Y. Yang and J. Pedersen. A comparative study on feature selection in text categorization. In ICML, 1997.
    • (1997) ICML
    • Yang, Y.1    Pedersen, J.2
  • 28
    • 0017493286 scopus 로고
    • A universal algorithm for sequential data compression
    • J. Ziv and A. Lempel. A universal algorithm for sequential data compression. TIT, 23(3):337-343, 1977.
    • (1977) TIT , vol.23 , Issue.3 , pp. 337-343
    • Ziv, J.1    Lempel, A.2
  • 29
    • 33745309913 scopus 로고    scopus 로고
    • Sparse principal component analysis
    • H. Zou, T. Hastie, and R. Tibshirani. Sparse principal component analysis. JCGS, 15(2):265-286, 2006.
    • (2006) JCGS , vol.15 , Issue.2 , pp. 265-286
    • Zou, H.1    Hastie, T.2    Tibshirani, R.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.