Semi-Supervised Learning by Local Behavioral Searching Strategy

Semi-supervised learning has attracted a significant amount of attention in pattern recognition and machine learning. Among these methods, a very popular type is semi-supervised support vector machines. However, parameter selection in heat kernel function during the learning process is troublesome and harms the performance improvement of the hypothesis. To solve this problem, a novel local behavioral searching strategy is proposed for semi-supervised learning in this paper. In detail, based on human behavioral learning theory, the support vector machine is regularized with the un-normalized graph Laplacian. After building local distribution of feature space, local behavioral paradigm considers the form of the underlying probability distribution in the neighborhood of a point. Validation of the proposed method is performed with extensive experiments. Results demonstrate that compared with traditional method, our method can more effectively and stably enhance the learning performance

Semi-Supervised Learning by Local Behavioral Searching Strategy

Abstract: Semi-supervised learning has attracted a significant amount of attention in pattern recognition and machine learning. Among these methods, a very popular type is semi-supervised support vector machines. However, parameter selection in heat kernel function during the learning process is troublesome and harms the performance improvement of the hypothesis. To solve this problem, a novel local behavioral searching strategy is proposed for semi-supervised learning in this paper. In detail, based on human behavioral learning theory, the support vector machine is regularized with the un-normalized graph Laplacian. After building local distribution of feature space, local behavioral paradigm considers the form of the underlying probability distribution in the neighborhood of a point. Validation of the proposed method is performed with extensive experiments. Results demonstrate that compared with traditional method, our method can more effectively and stably enhance the learning performance

Community-based matrix factorization for scalable music recommendation on smartphones

Mobile karaoke has attracted more attention as a popular mobile entertainment and social network platform, where music recommendations are highly desired to improve its user experiences. Traditional music recommendation methods suffer from the data sparsity issue and usually ignore the social interactions among users. In this paper, we propose a novel parallel community-based matrix factorization method which exploits implicit user behavior data to model user preferences from both social level, via community detection, and individual level. Both offline evaluation on a real dataset from Changba and online traffic investigations show the effectiveness of our method.EICPCI-S(ISTP)[email protected]; [email protected]; [email protected]; [email protected]; [email protected]; [email protected]

Effects of plastic film mulching and legume rotation on soil nutrients and microbial communities in the Loess Plateau of China

Abstract Background Potato (Solanum tuberosum L.) continuous cropping causes the decrease of tuber yield, deterioration of quality and soil degradation in the semi-arid area. These negative effects can generally be mitigated by legume rotation and mulching. However, little is known about how can mulching and legume rotation alleviate the above damage through altering soil environment. Methods A field experiment was conducted to investigate changes in soil properties and microbial community in response to legume rotation and mulching under six planting patterns: potato continuous cropping without film mulching (PC), potato continuous cropping with film mulching (PCF), potato–broad bean rotation without film mulching (R1), potato–broad bean rotation with film mulching (R1F), potato–pea rotation without film mulching (R2) and potato–pea rotation with film mulching (R2F). Results Compared with the PC, the R1F and R2F had significantly enhanced the contents of alkaline nitrogen (AN), available phosphorus (AP), available potassium (AK), total carbon (TC) and total nitrogen (TN), but reduced soil pH and electrical conductivity (EC). The Shannon index of fungi in R1F and R2 was significantly higher than other treatments. The dominant bacterial and fungal phyla of each treatment was Proteobacteria and Ascomycota. R1, R1F, R2 and R2F enhanced the relative abundance of metabolic fungi and altered key differential microbial species. Soil EC, AN and AK were major factors influencing the soil bacterial and fungal communities. Conclusion Overall, the study demonstrated that potato-broad bean/pea rotation with mulching can be adopted as the preferred cropping systems to alleviate potato continuous cropping obstacles through enhancing soil fertility and regulating soil microbial communities in the semi-arid of Loess Plateau, China. Graphical Abstrac