On prediction of individual sequences

Sequential randomized prediction of an arbitrary binary sequence isinvestigated. No assumption is made on the mechanism of generating the bit sequence. The goal of the predictor is to minimize its relative loss, i.e., to make (almost) as few mistakes as the best ``expert'' in a fixed, possibly infinite, set of experts. We point out a surprising connection between this prediction problem and empirical process theory. First, in the special case of static (memoryless) experts, we completely characterize the minimax relative loss in terms of the maximum of an associated Rademacher process. Then we show general upper and lower bounds on the minimaxrelative loss in terms of the geometry of the class of experts. As main examples, we determine the exact order of magnitude of the minimax relative loss for the class of autoregressive linear predictors and for the class of Markov experts.

Individual Classification of Mild Cognitive Impairment Subtypes by Support Vector Machine Analysis of White Matter DTI.

BACKGROUND AND PURPOSE: MCI was recently subdivided into sd-aMCI, sd-fMCI, and md-aMCI. The current investigation aimed to discriminate between MCI subtypes by using DTI. MATERIALS AND METHODS: Sixty-six prospective participants were included: 18 with sd-aMCI, 13 with sd-fMCI, and 35 with md-aMCI. Statistics included group comparisons using TBSS and individual classification using SVMs. RESULTS: The group-level analysis revealed a decrease in FA in md-aMCI versus sd-aMCI in an extensive bilateral, right-dominant network, and a more pronounced reduction of FA in md-aMCI compared with sd-fMCI in right inferior fronto-occipital fasciculus and inferior longitudinal fasciculus. The comparison between sd-fMCI and sd-aMCI, as well as the analysis of the other diffusion parameters, yielded no significant group differences. The individual-level SVM analysis provided discrimination between the MCI subtypes with accuracies around 97%. The major limitation is the relatively small number of cases of MCI. CONCLUSIONS: Our data show that, at the group level, the md-aMCI subgroup has the most pronounced damage in white matter integrity. Individually, SVM analysis of white matter FA provided highly accurate classification of MCI subtypes.

Iowa Individual Income Tax School District, Tax Year 1997

Iowa School District Surtax Report 1997

Iowa Individual Income Tax School District, Tax Year 1998

Iowa School District Surtax Report 1998

Iowa Individual Income Tax School District Report, Tax Year 1999

Iowa School District Surtax Report 1999

Iowa Individual Income Tax School District Report, Tax Year 2000

Iowa School District Surtax Report 2000

Iowa Individual Income Tax School District Report, Tax Year 2001

Iowa School District Surtax Report 2001

Iowa Individual Income Tax School District Report, Tax Year 2002

Iowa School District Surtax Report 2002

Iowa Individual Income Tax School District Report, Tax Year 2003

Iowa School District Surtax Report 2003

Iowa Individual Income Tax School District Report, Tax Year 2004

Iowa School District Surtax Report 2004

Iowa Individual Income Tax School District Report, Tax Year 2005

Iowa School District Surtax Report 2005

Iowa Individual Income Annual Tax Statistical Report 1997

Iowa Individual Income Tax Statistical Report 1997

Iowa Individual Income Tax Annual Statistical Report 1998

Iowa Individual Income Tax Statistical Report 1998

Iowa Individual Income Tax Annual Statistical Report 1999

Iowa Individual Income Tax Statistical Report 1999

Iowa Individual Income Tax Annual Statistical Report 2000

Iowa Individual Income Tax Statistical Report 2000