Bagging predictors is a method for generating multiple versions of a predictor and using these to get an aggregated predictor. The aggregation averages over the versions when predicting a numerical outcome and does a plurality vote when predicting a class. The multiple versions are formed

next. To increase parallelism, several authors have suggested ways of predicting the direction of conditional branches with hardware that uses the history of previous branches. The different proposed predictors take advan-tage of different observed patterns in branch behavior. This paper presents a

, where strongly correlated predictors tend to be in (out) the model together. The elastic net is particularly useful when the number of predictors (p) is much bigger than the number of observations (n). By contrast, the lasso is not a very satisfactory variable selection method in the p n case

Abstract. Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees

to the period 1961–90, describes an extended suite of surface climate variables, explicitly incorporates elevation as a predictor variable, and contains an evaluation of regional errors associated with this and other commonly used climatologies. The climatology is already being used by researchers in the areas

objective function. Unlike most of previous approaches which typically decompose a multiclass problem into multiple independent binary classification tasks, our notion of margin yields a direct method for training multiclass predictors. By using the dual of the optimization problem we are able

-regulation, self-efficacy, and test anxiety emerged as the best predictors of performance. Intrinsic value did not have a direct influence on performance but was strongly related to self-regulation and cognitive strategy use, regardless of prior achievement level. The implications of individual differences

. Validation was performed using finiteelement simulation. The paper also introduces several effective methods for dynamic thermal management (DTM): “temperaturetracking” frequency scaling, localized toggling, and migrating computation to spare hardware units. Modeling temperature at the microarchitecture

matrix can be computed by the Nyström method (which is used for the numerical solution of eigenproblems). This is achieved by carrying out an eigendecomposition on a smaller system of size m < n, and then expanding the results back up to n dimensions. The computational complexity of a predictor using

is distinguished by prefetch-ing multiple reference predictions from the memory subsystem, and then prioritizing the delivery of those references to the processor. This design results in a prefetching system that provides good coverage, is accurate and produces timely results that can be ef-fectively used