We study a class of algorithms that speed up the training process of support vector machines (SVMs) by returning an approximate SVM. We focus on algorithms that reduce the size of...
Finding discords in time series database is an important problem in a great variety of applications, such as space shuttle telemetry, mechanical industry, biomedicine, and financ...
Yingyi Bu, Oscar Tat-Wing Leung, Ada Wai-Chee Fu, ...
Decision lists (or ordered rule sets) have two attractive properties compared to unordered rule sets: they require a simpler classification procedure and they allow for a more co...
We present a new approach for dealing with distribution change and concept drift when learning from data sequences that may vary with time. We use sliding windows whose size, inst...
Consensus clustering has emerged as one of the principal clustering problems in the data mining community. In recent years the theoretical computer science community has generated...
A number of real-world domains such as social networks and e-commerce involve heterogeneous data that describes relations between multiple classes of entities. Understanding the n...
Topic modeling techniques have widespread use in text data mining applications. Some applications use batch models, which perform clustering on the document collection in aggregat...
Logistic models are arguably one of the most widely used data analysis techniques. In this paper, we present analyses focussing on two important aspects of logistic models—its r...
In many applications, we monitor data obtained from multiple streaming sources for collective decision making. The task presents several challenges. First, data in sensor networks...
We study the joint feature selection problem when learning multiple related classification or regression tasks. By imposing an automatic relevance determination prior on the hypo...
Tao Xiong, Jinbo Bi, R. Bharat Rao, Vladimir Cherk...