On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,181 papers · 148 categories

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6 results for “SAMME.R”

Automatic Model Monitoring for Data Streamscs.LG

SAMM monitors model drift in data streams without labels.

problem Detecting concept drift in unsupervised data streams.

method Time and space efficient unsupervised streaming algorithm for drift detection; generates explanations for drift.

result SAMM detects useful anomalous events for fraud detection.

concept drift data streams unsupervised learning model monitoring fraud detection

2019-08-12abs ↗pdf ↗

The SAMME.C2 algorithm for severely imbalanced multi-class classificationstat.ML

New algorithm boosts classification for imbalanced data.

problem Accurately classifying observations in severely imbalanced datasets.

method SAMME.C2 algorithm blending boosting and cost-sensitive techniques.

result Consistently superior performance in imbalanced classification problems.

classification imbalanced data boosting cost-sensitive multi-class

2021-12-30abs ↗pdf ↗

Cost-sensitive Multi-class AdaBoost for Understanding Driving Behavior with Telematicsstat.AP

Improved risk assessment for UBI using telematics data and AdaBoost.

problem Class imbalance in predicting claims frequency for UBI.

method Cost-sensitive multi-class AdaBoost (SAMME.C2) algorithm.

result SAMME.C2 outperforms other models in handling class imbalances.

AdaBoost UBI Telematics Risk assessment Class imbalance

2020-07-06abs ↗pdf ↗

Iterative Weak Learnability and Multi-Class AdaBooststat.ML

Improved multi-class AdaBoost algorithm with stronger weak learnability condition.

problem Multi-class classification problem with at least two labels.

method Recursive ensemble algorithm inspired by SAMME, strengthening weak learnability condition.

result Final hypothesis converges to correct label with probability 1 and generalization error bounds exponentially.

AdaBoost Multi-class classification Weak learnability Recursive ensemble Generalization error

2021-01-26abs ↗pdf ↗

Dense Adaptive Cascade Forest: A Self Adaptive Deep Ensemble for Classification Problemscs.LG

daForest improves deep forest ensemble for classification with better performance.

problem Improving classification accuracy with small training sets.

method SAMME.R boosting, feed-forward connection, hyper-parameters optimization.

result daForest outperforms neural networks and achieves state-of-the-art results.

deep forest SAMME.R feed-forward hyper-parameters classification

2018-04-29abs ↗pdf ↗

Neural Networks for Complex Datacs.NE

Artificial neural networks are simple and efficient machine learning tools. Defined originally in the traditional setting of simple vector data, neural network models have evolved to address more and more difficulties of complex real world problems, ranging from time evolving data to sophisticated data structures such …

2012-10-24abs ↗pdf ↗