We investigate the generalization performance of some learning problems in Hilbert functional Spaces. We introduce a notion of convergence of the estimated functional predictor to the best underlying predictor, and obtain an estimate on the rate of the convergence. This estimate allows us to derive generalization bounds on some learning formulations.

This paper discusses a transductive version of conformal predictors. This version is computationally inefficient for big test sets, but it turns out that apparently crude “Bonferroni predictors” are about as good in their information efficiency and vastly superior in computational efficiency.

Flow experience is associated with learning motivation, performance and positive affect. Therefore it is important to analyze its antecedents. An important antecedent for experiencing flow is the balance between the person’s skill and how challenging the situation is (Csikszentmihalyi, 1990). According to Atkinson’s (1957) risk-taking model, only individuals with high hope-of-success prefer sit...

Experiments to determine the potential for program-level and/or phase-level adaptation of branch predictor configuration for the purpose of total processor energy savings were performed. The performance and energyefficiency of an 8-wide issue, out-of-order processor with six different branch predictors were evaluated on the SPECcpu2000 benchmark suite. Each branch predictor was compared to the ...

A development of a method for tracking visual contours is described. Given an “untrained” tracker, a training motion of an object can be observed over some extended time and stored as an image sequence. The image sequence is used to learn parameters in a stochastic differential equation model. These are used, in turn, to build a tracker whose predictor imitates the motion in the training set. T...

is called the regression function (of Y on X). The basic goal in nonparametric regression is to construct an estimate f̂ of f0, from i.i.d. samples (x1, y1), . . . (xn, yn) ∈ R × R that have the same joint distribution as (X,Y ). We often call X the input, predictor, feature, etc., and Y the output, outcome, response, etc. Importantly, in nonparametric regression we do not assume a certain param...