메뉴 건너뛰기




Volumn , Issue , 2007, Pages 1585-1592

Hyperparameter learning for graph based semi-supervised learning algorithms

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION ALGORITHM; GRADIENT-BASED METHOD; GRAPH-BASED; HARMONIC ENERGY; HYPER-PARAMETER; HYPERPARAMETERS; LABELED DATA; LEARNING METHODS; LEAVE-ONE-OUT; MATRIX INVERSIONS; PRE-COMPUTATION; PREDICTION ERRORS; SEMI-SUPERVISED LEARNING; UNLABELED DATA;

EID: 84864033982     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (33)

References (13)
  • 1
    • 84864060454 scopus 로고    scopus 로고
    • Combining graph laplacians for semi-supervised learning
    • Vancouver, Canada
    • Andreas Argyriou, Mark Herbster, and Massimiliano Pontil. Combining Graph Laplacians for Semi-Supervised Learning. In NIPS 2005, Vancouver, Canada, 2005.
    • (2005) NIPS 2005
    • Argyriou, A.1    Herbster, M.2    Pontil, M.3
  • 3
    • 0010805362 scopus 로고    scopus 로고
    • Learning from labeled and unlabeled data using graph mincuts
    • Avrin Blum, and Shuchi Chawla. Learning From Labeled and Unlabeled Data using Graph Mincuts. In ICML 2001.
    • ICML 2001
    • Blum, A.1    Chawla, S.2
  • 4
    • 84898939894 scopus 로고    scopus 로고
    • Proximity graphs for clustering andmanifold learning
    • Miguel Á Carreira-Perpiňán, and Richard S. Zemel. Proximity Graphs for Clustering andManifold Learning. In NIPS 2004.
    • NIPS 2004
    • Carreira-Perpiňán, M.A.1    Zemel, R.S.2
  • 5
    • 0036161011 scopus 로고    scopus 로고
    • Choosing multiple parameters for support vector machines
    • Olivier Chapelle, Vladimir Vapnik, Olivier Bousquet, and Sayan Mukherjee. Choosing Multiple Parameters for Support Vector Machines. Machine Learning, 46, 131-159, 2002.
    • (2002) Machine Learning , vol.46 , pp. 131-159
    • Chapelle, O.1    Vapnik, V.2    Bousquet, O.3    Mukherjee, S.4
  • 7
    • 35548937746 scopus 로고    scopus 로고
    • Semi-supervised learning of mixture models and bayesian networks
    • Fabio G. Cozman, Ira Cohen, and Marcelo C. Cirelo. Semi-Supervised Learning of Mixture Models and Bayesian Networks. In ICML 2003.
    • ICML 2003
    • Cozman, F.G.1    Cohen, I.2    Cirelo, M.C.3
  • 8
    • 1942484960 scopus 로고    scopus 로고
    • Transductive learning via spectral graph partitioning
    • Thorsten Joachims. Transductive Learning via Spectral Graph Partitioning. In ICML 2003.
    • ICML 2003
    • Joachims, T.1
  • 9
    • 84864039857 scopus 로고    scopus 로고
    • Hyperparameter and kernel learning for graph based semi-supervised classification
    • Ashish Kapoor, Yuan Qi, Hyungil Ahn, and Rosalind Picard. Hyperparameter and Kernel Learning for Graph Based Semi-Supervised Classification. In NIPS 2005.
    • NIPS 2005
    • Kapoor, A.1    Qi, Y.2    Ahn, H.3    Picard, R.4
  • 10
    • 14344259597 scopus 로고    scopus 로고
    • Kernels and regularization on graphs
    • Alexander Smola, and Risi Kondor. Kernels and Regularization on Graphs. In COLT 2003.
    • COLT 2003
    • Smola, A.1    Kondor, R.2
  • 11
    • 1942484430 scopus 로고    scopus 로고
    • Semi-supervised learning using gaussian fields and harmonic functions
    • Xiaojin Zhu, Zoubin Ghahramani, and John Lafferty. Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions. In ICML 2003.
    • ICML 2003
    • Zhu, X.1    Ghahramani, Z.2    Lafferty, J.3
  • 13
    • 84899028404 scopus 로고    scopus 로고
    • Non-parametric transforms of graph Kernels for semi-supervised learning
    • Xiaojin Zhu, Jaz Kandola, Zoubin Ghahramani, and John Lafferty. Non-parametric Transforms of Graph Kernels for Semi-Supervised Learning. In NIPS 2004.
    • NIPS 2004
    • Zhu, X.1    Kandola, J.2    Ghahramani, Z.3    Lafferty, J.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.