Thesiss

Temporal text data is often generated by a time-changing process or distribution. Such a drift in the underlying distribution cannot be captured by stationary likelihood techniques. We consider the application of local likelihood methods to generative and conditional modeling of…

In this dissertation we propose solutions to four major problems encountered by data stream classification, namely, infinite length, concept-drift, concept-evolution and limited labeled data . Traditional data stream classification techniques address only the infinite length and…