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This study examined individuad differences in the preference for and effectiveness of the type of attentional focus for motor learning. In two experiments, participants practicing a balance task (stabilometer) were asked to find out whether focusing on their feet (internal focus) or on two markets in front of their feet (external focus) was more effective.(More)
We propose methods for outlier handling and noise reduction using weighted local linear smoothing for a set of noisy points sampled from a nonlinear manifold. Weighted PCA is used as a building block for our methods and we suggest an iterative weight selection scheme for robust local linear fitting together with an outlier detection method based on minimal(More)
In 2 experiments, the authors manipulated the frequency of concurrent feedback to discern the effects on learning. In each experiment, participants (N = 48, Experiment 1; N = 36, Experiment 2) attempted to reproduce a criterion force-production waveform (5 s in duration) presented on the computer monitor. Consistent with the guidance hypothesis, the results(More)
The effects of an auditory model on the learning of relative and absolute timing were examined. In 2 experiments, participants attempted to learn to produce a 1,000- or 1,600-ms sequence of 5 key presses with a specific relative-timing pattern. In each experiment, participants were, or were not, provided an auditory model that consisted of a series of tones(More)
we propose a fully automatic system for cardiac view classiication of echocardiogram. Given an echo study video sequence, the system outputs a view label among the pre-deened standard views. The system is built based on a machine learning approach that extracts knowledge from an annotated database. It characterizes three features: 1) integrating local and(More)
In this paper, we propose a robust motion segmentation method based on the matrix factorization and subspace separation. We, first, mathematically prove that the shape interaction matrix can be derived using QR decomposition rather than Singular Value Decompo-sition(SVD). Using shape interaction matrix, we solve the motion seg-mentation problem using(More)
Various forms of boosting techniques have been popularly used in many data mining and machine learning related applications. Inspite of their great success, boosting algorithms still suffer from a few open-ended problems that require closer investigation. The efficiency of any such ensemble technique significantly relies on the choice of the weak learners(More)
In this paper, we propose a new support vector clustering (SVC) strategy by combining (SVC) with spectral graph partitioning (SGP). SVC has two main steps: support vector computation and cluster labeling using adjacency matrix. Spectral graph partitioning (SGP) method is applied to the adjacency matrix to determine the cluster labels. It is feasible to(More)