Kernel k-means clustering can correctly identify and extract a far more varied collection of cluster structures than the linear k-means clustering algorithm. However, kernel kmeans clustering is computationally expensive when the non-linear feature map is highdimensional and there are many input points. Kernel approximation, e.g., the Nyström method, has been applied in previous works to approx...

this paper presents a fuzzy decision-making approach to deal with a clustering supplier problem in a supply chain system. during recent years, determining suitable suppliers in the supply chain has become a key strategic consideration. however, the nature of these decisions is usually complex and unstructured. in general, many quantitative and qualitative factors, such as quality, price, and fl...

Clustering is a global approach to information classification, with applications in data mining, pattern recognition, image processing, bioinformatics, city planning, and more. Clustering is the partition of data into meaningful groups. Since the concept of “meaningful groups” varies widely among applications, there is a wide variety of clustering techniques. Broadly, these techniques can be ca...

Multiple instance learning (MIL) is a branch of machine learning that attempts to learn information from bags of instances. Many real-world applications such as localized content-based image retrieval and text categorization can be viewed as MIL problems. In this paper, we propose a new graph-based semi-supervised learning approach for multiple instance learning. By defining an instance-level g...

We consider the problem of verifying the identity of a distribution: Given the description of a distribution over a discrete support p = (p1, p2, . . . , pn), how many samples (independent draws) must one obtain from an unknown distribution, q, to distinguish, with high probability, the case that p = q from the case that the total variation distance (L1 distance) ||p− q||1 ≥ ε? We resolve this ...

The goal of traditional multi-instance learning (MIL) is to predict the labels of the bags, whereas in many real applications, it is desirable to get the instance labels, especially the labels of key instances that trigger the bag labels, in addition to getting bag labels. Such a problem has been largely unexplored before. In this paper, we formulate the Key Instance Detection (KID) problem, an...

Multi-instance (MI) learning is a variant of supervised machine learning, where each learning example contains a bag of instances instead of just a single feature vector. MI learning has applications in areas such as drug activity prediction, fruit disease management and image classification. This thesis investigates the case where each instance has a weight value determining the level of influ...

Multiple-Instance Learning (MIL) has attracted much attention of the machine learning community in recent years and many real-world applications have been successfully formulated as MIL problems. Over the past few years, several Instance Selection-based MIL (ISMIL) algorithms have been presented by using the concept of the embedding space. Although they delivered very promising performance, the...

Wei Li1 [email protected] Changhu Wang2 [email protected] Lei Zhang3 [email protected] Yong Rui2 [email protected] Bo Zhang1 [email protected] 1 State Key Lab of Intelligent Technology and Systems, TNList, Department of Computer Science and Technology, Tsinghua University Beijing 100084, China 2 Microsoft Research No. 5 Danling Street, Haidian District, Beijing 100080, China...