Predicting Lessee Switch Behavior using Logit Models

Predicting Behavior Patterns Using Adaptive Workload Models

Workload characteristics in a modern networking environment are very dynamic. In order to maximize performance continuously, it is natural to explore the possibility of intelligent systems which can take cognizance of the dynamics in workload and adapt themselves for future control applications. In this paper, we propose a mechanism which represents the previous state of the system as a string....

Predicting Customer Models Using Behavior-Based Features in Shops

Recent sensor technologies have enabled the capture of users’ behavior data. Given the large amount of data currently available from sensor-equipped environments, it is important to attempt characterization of the sensor data for automatically modeling users in a ubiquitous and mobile computing environment. As described herein, we propose a method that predicts a customer model using features b...

Predicting Web Cache Behavior using Stochastic State-Space Models

Web caches play an important role in improving the surfing experience of Web clients and reducing the network traffic seen by Web servers. Accurate analytical models of Web caches are desirable as they can provide inexpensive ways to make resource provisioning decisions at a cache itself as well as at the Web servers it is servicing. Explicitly modeling a Web cache has two major shortcomings: (...

Conditional Logit Models

Contour Integration Models Predicting Human Behavior

Contour integration is believed to be a fundamental process in object recognition and image segmentation. However, its neuronal mechanisms are still not well understood. Psychophysical experiments showed that humans are remarkably efficient in integrating contours even if these are jittered or partially occluded. Therefore the brain requires a reliable algorithm for extracting contours from sti...