A Simple Algorithm for Semi-supervised Learning with Improved Generalization Error Bound

In this work, we develop a simple algorithm for semi-supervised regression. The key idea is to use the top eigenfunctions of integral operator derived from both labeled and unlabeled examples as the basis functions and learn the prediction function by a simple linear regression. We show that under appropriate assumptions about the integral operator, this approach is able to achieve an improved regression error bound better than existing bounds of supervised learning. We also verify the effectiveness of the proposed algorithm by an empirical study.Comment: Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012

KINEMATIC ANALYSIS OF LOWER EXTREMITY ON DIFFERENT RUNNING SPEED

A kinematic analysis of selected variables of human lower extremity was completed during different running speed (3.5±O.3 and 4.9±O.2 m/s)on treadmill.Ten male elite running players served as subjects.It got two dimensions photograph analysis along sagittal plane with video data collected at 60Hz.The purpose of the study was to analyze the kinematic parameters including the range of motion,the joint angle,angular velocity and angular acceleration of lower extremity joint related to the cycle time during horizontal running. According to the experimental result of a research ,we could get the below conclusion:angle of lower-extremityed joints in different running speed reaches significant difference. Angle of lower-extremityed joints is getting more with the increasing of velocity.Then maximum angle, angular velocity and angular acceleration of knee joints stretched are getting more with the increasing of velocity. Maximum stretched angle in joint extension phase of take-off significnce