Breiman, L. (2001) Random Forests. Machine Learning, 45, 5-32. - References - Scientific Research Publishing Home References Follow SCIRP Contact us [email protected] +86 18163351462 (WhatsApp) ...

Aims To develop prediction models for identifying cases with poor visual outcomes after surgery for primary rhegmatogenous ...

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摘要：骨骼残骸的性别估算是法医人类学(Forensic Anthropology)的关键环节，其中颅骨与骨盆是形态学上两性异形(Sexual Dimorphism)最显著的部位。传统多元统计方法如判别函数分析(Discriminant Function Ana 摘要：骨骼残骸的性别估算是法医人类学(Forensic Anthropology)的关键环节，其中颅骨与骨盆是形态学上两性异形(Sexua ...

Abstract: Founded in 2008 Airbnb has become an attractive alternative within the hospitality industry as an online booking service based in San Francisco, California. It has grown since then and ...

Supervised machine learning improves predictions of compressive strength in industrial waste-modified concrete, supporting ...

This project applies Machine Learning (XGBoost, Random Forest) and Deep Learning (LSTM) to predict the Water Quality Index (WQI) using synthetic environmental data, helping assess water safety, ...

As humans, our eyes take in two-dimensional images that our brains convert to three-dimensional experiences. This ability ...

IBM has published a patent application describing an AI decision engine for managing 3D printers. That is a pretty wide ...

Seeking to empower smallholder farmers, an Asia Pacific University of Technology & Innovation (APU) duo devised a solution ...

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.