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Learning from Time-Changing Data with Adaptive Windowing
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
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
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)
- N. Bshouty, R. Cleve, Ricard Gavaldà, Sampath Kannan, C. Tamon
- Computer Science, MathematicsCOLT '94
- 16 July 1994
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
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.
Adaptive Sampling Methods for Scaling Up Knowledge Discovery Algorithms
- Carlos Domingo, Ricard Gavaldà, O. Watanabe
- Computer ScienceData Mining and Knowledge Discovery
- 1 December 1999
This paper proposes an adaptive sampling method that solves a general problem covering many actual problems arising in applications of discovery science, and proves the correctness of the method and estimates its efficiency theoretically.
Building Green Cloud Services at Low Cost
- Josep Lluís Berral, Íñigo Goiri, Thu D. Nguyen, Ricard Gavaldà, J. Torres, R. Bianchini
- Computer ScienceIEEE 34th International Conference on Distributed…
- 30 June 2014
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.
Self-adaptive utility-based web session management
Online Techniques for Dealing with Concept Drift in Process Mining
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
- Josep Lluís Berral, Ricard Gavaldà, J. Torres
- Computer ScienceIEEE/ACM 12th International Conference on Grid…
- 21 September 2011
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.