A privacy-preserving marketing framework applies homomorphic encryption to perform machine learning on encrypted ...

But unlike most quants, I run a concentrated, fundamentals-based portfolio. More than 50% of my fund is invested in only eight companies, and they're the kinds of stocks that Peter Lynch and Charlie ...

Machine learning algorithms create potentially more accurate models than linear models, but any increase in accuracy over more traditional, better-understood, and more easily explainable techniques is ...

Support vector regression can predict numeric values effectively, and this article shows how to implement and train a kernel SVR model in C# using stochastic sub-gradient descent.

This project builds a fraud detection system using Gradient Boosting and XGBoost on 200,000+ real credit card transactions. The core challenge is extreme class imbalance — only 0.25% of transactions ...

We use positive and unlabeled (PU) learning to address this challenge. Objective: This study aims to identify US Veterans whose self-harm events were not explicitly captured through diagnostic codes ...

For example, GBDT may automatically identify that the combination of “high HII and low GCS” significantly increases the risk of rebleeding, whereas in traditional logistic regression, such an ...