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Found 6 papers, 3 papers with code
No Pattern, No Recognition: a Survey about Reproducibility and Distortion Issues of Text Clustering and Topic Modeling
Document categorization and information retrieval are two applications that may benefit from unsupervised learning (e. g., text clustering and topic modeling), including exploratory data analysis.
Rethinking Default Values: a Low Cost and Efficient Strategy to Define Hyperparameters
Our approach is grounded on a small set of optimized values able to obtain predictive performance values better than default settings provided by popular tools.
A meta-learning recommender system for hyperparameter tuning: predicting when tuning improves SVM classifiers
For many machine learning algorithms, predictive performance is critically affected by the hyperparameter values used to train them.
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
Better Trees: An empirical study on hyperparameter tuning of classification decision tree induction algorithms
DT induction algorithms present high predictive performance and interpretable classification models, though many HPs need to be adjusted.
Strict Very Fast Decision Tree: a memory conservative algorithm for data stream mining
Thus, SVFDT is a suitable option for data stream mining with memory and time limitations, recommended as a weak learner in ensemble-based solutions.
