报告题目：Adaptive Dimension Reduction Using Discriminant Analysis and K-meansClustering

报 告 人：王永胜

报告时间：2017年5月24日（周三） 下午 2:00

报告地点：贵州大学北校区博学楼603室

报告内容摘要：

In this paper , We combine linear discriminant analysis (LDA) and K-means clustering into a coherent framework to adaptively select the most discriminative subspace. We use K-means clustering to generate class labels and use LDA to do subspace selection. The clustering process is thus integrated with the subspace selection process and the data are then simultaneously clustered while the feature subspaces are selected.

报告题目：AutoPerf: Automated Load Testing and Resource Usage Profiling of Multi-Tier Internet Applications

报 告 人：孙凯俐

报告时间：2017年5月24日（周三） 下午 2:00

报告地点：贵州大学北校区博学楼603室

报告内容摘要：

 In this paper, we present AutoPerf, a load generator tool designed to meet two distinct goals, named capacity analysis and profiling. Results show that AutoPerf is able to run performance tests very efficiently while still producing an accurate chart of performance metrics.