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Learning from Time-Changing Data with Adaptive Windowing
TLDR
A new approach for dealing with distribution change and concept drift when learning from data sequences that may vary with time is presented, using sliding windows whose size is recomputed online according to the rate of change observed from the data in the window itself.
New ensemble methods for evolving data streams
TLDR
A new experimental data stream framework for studying concept drift, and two new variants of Bagging: ADWIN Bagging and Adaptive-Size Hoeffding Tree (ASHT) Bagging are proposed.
Adaptive Learning from Evolving Data Streams
TLDR
A method for developing algorithms that can adaptively learn from data streams that drift over time, based on using change detectors and estimator modules at the right places and choosing implementations with theoretical guarantees in order to extend such guarantees to the resulting adaptive learning algorithm.
Oracles and queries that are sufficient for exact learning (extended abstract)
TLDR
There is a randomized polynomial-time algorithm that learns any class that is learnable from membership queries with unlimited computational power.
Towards energy-aware scheduling in data centers using machine learning
TLDR
This work proposes a framework that provides an intelligent consolidation methodology using different techniques such as turning on/off machines, power-aware consolidation algorithms, and machine learning techniques to deal with uncertain information while maximizing performance in an energy-efficient data center.
Building Green Cloud Services at Low Cost
TLDR
This paper proposes a framework, optimization problem, and solution approach for sitting and provisioning green data centers for a follow-the-renewables HPC cloud service, and illustrates the location selection tradeoffs by quantifying the minimum cost of achieving different amounts of renewable energy.
Online Techniques for Dealing with Concept Drift in Process Mining
TLDR
This paper presents the first online mechanism for detecting and managing concept drift, which is based on abstract interpretation and sequential sampling, together with recent learning techniques on data streams.
Adaptive Scheduling on Power-Aware Managed Data-Centers Using Machine Learning
TLDR
Experiments show that machine learning algorithms can predict system behavior with acceptable accuracy, and that their combination with the exact or approximate schedulers manages to allocate tasks to hosts striking a balance between revenue for executed tasks, quality of service, and power consumption.
A methodology for the evaluation of high response time on E-commerce users and sales
TLDR
This work presents a methodology to determine how user sales are affected as response time increases, and presents the evaluation of high response time on users for popular applications found in the Web.
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