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Found 7 papers, 3 papers with code
On Taking Advantage of Opportunistic Meta-knowledge to Reduce Configuration Spaces for Automated Machine Learning
The automated machine learning (AutoML) process can require searching through complex configuration spaces of not only machine learning (ML) components and their hyperparameters but also ways of composing them together, i. e. forming ML pipelines.
An Efficient Video Streaming Architecture with QoS Control for Virtual Desktop Infrastructure in Cloud Computing
In virtual desktop infrastructure (VDI) environments, the remote display protocol has a big responsibility to transmit video data from a data center-hosted desktop to the endpoint.
Exploring Opportunistic Meta-knowledge to Reduce Search Spaces for Automated Machine Learning
Machine learning (ML) pipeline composition and optimisation have been studied to seek multi-stage ML models, i. e. preprocessor-inclusive, that are both valid and well-performing.
Incremental Search Space Construction for Machine Learning Pipeline Synthesis
We prove the effectiveness and competitiveness of our approach on 28 data sets used in well-established AutoML benchmarks in comparison with state-of-the-art AutoML frameworks.
AutoWeka4MCPS-AVATAR: Accelerating Automated Machine Learning Pipeline Composition and Optimisation
Instead of executing the original ML pipeline to evaluate its validity, the AVATAR evaluates its surrogate model constructed by capabilities and effects of the ML pipeline components and input/output simplified mappings.
Improving The Performance Of The K-means Algorithm
The Incremental K-means (IKM), an improved version of K-means (KM), was introduced to improve the clustering quality of KM significantly.
AVATAR -- Machine Learning Pipeline Evaluation Using Surrogate Model
The evaluation of machine learning (ML) pipelines is essential during automatic ML pipeline composition and optimisation.
