Multi-Label Learning with Global and Local Label Correlation

Abstract:

It is well-known that exploiting label correlations is important to multi-label learning. Existing approaches either assume that the label correlations Read More

Multi-instance Learning with Discriminative Bag Mapping

Abstract:

Multi-instance learning (MIL) is a useful tool for tackling labeling ambiguity in learning because it allows a bag of instances Read More

Minority Oversampling in Kernel Adaptive Subspaces for Class Imbalanced Datasets

Abstract:

The class imbalance problem in machine learning occurs when certain classes are underrepresented relative to the others, leading to a Read More

Privacy-preserving Image Processing in the Cloud

Abstract:

Millions of private images are generated in various digital devices every day. The consequent massive computational workload makes people turn Read More

Optimizing for Tail Sojourn Times of Cloud Clusters

Abstract:

A common pitfall when hosting applications on today’s cloud environments is that virtual servers often experience varying execution speeds due Read More

Modelling and Analysis of A Novel Deadline-Aware Scheduling Scheme For Cloud Computing Data Centers

Abstract:

User Request (UR) service scheduling is a process that significantly impacts the performance of a cloud data center. This is Read More

Multi-attributed Graph Matching with Multi-layer Graph Structure and Multi-layer Random Walks

Abstract:

This paper addresses the multi-attributed graph matching problem, which considers multiple attributes jointly while preserving the characteristics of each attribute Read More

Monte-Carlo Acceleration of Bilateral Filter and Non-Local Means

Abstract:

We propose stochastic bilateral filter (SBF) and stochastic non-local means (SNLM), efficient randomized processes that agree with conventional bilateral filter Read More