Targeted Local Support Vector Machine for Age-Dependent Classification
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Donglin Zeng | Karen Marder | Yuanjia Wang | Huaihou Chen | Tianle Chen | D. Zeng | K. Marder | Yuanjia Wang | Huaihou Chen | Tianle Chen
[1] Guodong Guo,et al. Support Vector Machines Applications , 2014 .
[2] H. Zou,et al. The F ∞ -norm support vector machine , 2008 .
[3] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[4] Jane S. Paulsen,et al. Detection of Huntington’s disease decades before diagnosis: the Predict-HD study , 2007, Journal of Neurology, Neurosurgery, and Psychiatry.
[5] T. Cai,et al. Combining Predictors for Classification Using the Area under the Receiver Operating Characteristic Curve , 2006, Biometrics.
[6] Yi Lin,et al. Support Vector Machines and the Bayes Rule in Classification , 2002, Data Mining and Knowledge Discovery.
[7] Philip H. S. Torr,et al. Locally Linear Support Vector Machines , 2011, ICML.
[8] Runze Li,et al. Local Rank Inference for Varying Coefficient Models , 2009, Journal of the American Statistical Association.
[9] Personal factors associated with reported benefits of Huntington disease family history or genetic testing. , 2010, Genetic testing and molecular biomarkers.
[10] A. Mechelli,et al. Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: A critical review , 2012, Neuroscience & Biobehavioral Reviews.
[11] T. Foroud,et al. Differences in duration of Huntington’s disease based on age at onset , 1999, Journal of neurology, neurosurgery, and psychiatry.
[12] Jianqing Fan,et al. Efficient Estimation and Inferences for Varying-Coefficient Models , 2000 .
[13] Jane S. Paulsen,et al. Predictors of diagnosis in Huntington disease , 2007, Neurology.
[14] Robert Tibshirani,et al. 1-norm Support Vector Machines , 2003, NIPS.
[15] Philip H. S. Torr,et al. Learning Anchor Planes for Classification , 2011, NIPS.
[16] P. Celsis,et al. Age-related cognitive decline, mild cognitive impairment or preclinical Alzheimer's disease? , 2000, Annals of medicine.
[17] Wei Pan,et al. On Efficient Large Margin Semisupervised Learning: Method and Theory , 2009, J. Mach. Learn. Res..
[18] W. Wong,et al. On ψ-Learning , 2003 .
[19] Jane S. Paulsen,et al. Unified Huntington's disease rating scale: Reliability and consistency , 1996, Movement disorders : official journal of the Movement Disorder Society.
[20] M. Pepe,et al. Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. , 2004, American journal of epidemiology.
[21] Manish S. Shah,et al. A novel gene containing a trinucleotide repeat that is expanded and unstable on Huntington's disease chromosomes , 1993, Cell.
[22] Yufeng Liu,et al. Functional Robust Support Vector Machines for Sparse and Irregular Longitudinal Data , 2013, Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America.
[23] Gary Longton,et al. Limitations of the Odds Ratio in Gauging the Performance of a Diagnostic or Prognostic Marker , 2004 .
[24] Jane S. Paulsen,et al. Preparing for preventive clinical trials: the Predict-HD study. , 2006, Archives of neurology.
[25] Xiaodong Lin,et al. Gene expression Gene selection using support vector machines with non-convex penalty , 2005 .
[26] J. Ware. The limitations of risk factors as prognostic tools. , 2006, The New England journal of medicine.
[27] K. Boone,et al. Handbook of Normative Data for Neuropsychological Assessment , 1999 .
[28] E. Ray Dorsey,et al. Characterization of a Large Group of Individuals with Huntington Disease and Their Relatives Enrolled in the COHORT Study , 2012, PloS one.
[29] G. Wahba. Spline models for observational data , 1990 .
[30] Jane S. Paulsen,et al. Indexing disease progression at study entry with individuals at‐risk for Huntington disease , 2011, American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics.
[31] Joseph T. Glessner,et al. From Disease Association to Risk Assessment: An Optimistic View from Genome-Wide Association Studies on Type 1 Diabetes , 2009, PLoS genetics.
[32] Andreas Christmann,et al. Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.
[33] Ingo Steinwart,et al. Fast rates for support vector machines using Gaussian kernels , 2007, 0708.1838.
[34] Marti A. Hearst. Trends & Controversies: Support Vector Machines , 1998, IEEE Intell. Syst..