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Found 7 papers, 1 papers with code
MegazordNet: combining statistical and machine learning standpoints for time series forecasting
Forecasting financial time series is considered to be a difficult task due to the chaotic feature of the series.
River: machine learning for streaming data in Python
It is the result from the merger of the two most popular packages for stream learning in Python: Creme and scikit-multiflow.
Using dynamical quantization to perform split attempts in online tree regressors
QO can be easily integrated into incremental decision trees, such as Hoeffding Trees, and it has a monitoring cost of $O(1)$ per instance and sub-linear cost to evaluate split candidates.
Improved prediction of soil properties with Multi-target Stacked Generalisation on EDXRF spectra
In this study, we proposed the Multi-target Stacked Generalisation (MTSG), a novel MTR method relying on learning from different regressors arranged in stacking structure for a boosted outcome.
Towards meta-learning for multi-target regression problems
In this paper, we propose a meta-learning system to recommend the best predictive method for a given multi-target regression problem.
Online Local Boosting: improving performance in online decision trees
To increase predictive performance without largely increasing memory and time costs, this paper introduces a novel algorithm, named Online Local Boosting (OLBoost), which can be combined into online decision tree algorithms to improve their predictive performance without modifying the structure of the induced decision trees.
Online Multi-target regression trees with stacked leaf models
The proposed strategy extends existing online decision tree learning algorithm to explore inter-target dependencies while making predictions.
Ranked #4 on
Neural Network Compression
on CIFAR-10
