Labeling large scale social media data using budget-driven One-class SVM classification

The social media classification problems draw more and more attention in the past few years. With the rapid development of Internet and the popularity of computers, there is astronomical amount of information in the social network (social media platforms). The datasets are generally large scale and are often corrupted by noise. The presence of noise in training set has strong impact on the performance of supervised learning (classification) techniques. A budget-driven One-class SVM approach is presented in this thesis that is suitable for large scale social media data classification. Our approach is based on an existing online One-class SVM learning algorithm, referred as STOCS (Self-Tuning One-Class SVM) algorithm. To justify our choice, we first analyze the noise-resilient ability of STOCS using synthetic data. The experiments suggest that STOCS is more robust against label noise than several other existing approaches. Next, to handle big data classification problem for social media data, we introduce several budget driven features, which allow the algorithm to be trained within limited time and under limited memory requirement. Besides, the resulting algorithm can be easily adapted to changes in dynamic data with minimal computational cost. Compared with two state-of-the-art approaches, Lib-Linear and kNN, our approach is shown to be competitive with lower requirements of memory and time.

The role of supraspinal excitability in the development of healthy-limb deficits following anterior cruciate ligament injury

In the current study, we examined how supraspinal and spinal excitability were altered bilaterally after unilateral anterior cruciate ligament reconstruction (ACLr). 7 participants with ACLr and 7 healthy controls underwent transcranial magnetic stimulation (TMS) and electrical stimulation. To evaluate supraspinal excitability, resting motor thresholds (RMT) and motor evoked potential (MEP) stimulus response curves (SRC) were used. To measure spinal excitability, H-reflex SRC gain was assessed. Mixed factorial ANOVAs were used to compare measures between limbs and between groups. Cohen’s d was used to assess effect sizes between groups. Data indicated no significant differences between subject groups or between limbs. However, large effect sizes were found between limbs for H-reflex gain and RMTs suggesting that ACLr can have an effect on some of the variables examined. This study identified decreases in strength in the injured limbs and that subjects with an ACL injury exhibited decreases in spinal and supraspinal excitability of the quadriceps compared to Healthy controls.