This paper demonstrates the effectiveness of ensemble machine learning algorithms over the conventional multivariable linear regression models including Ordinary Least Squares, Robust Linear Model, and Lasso Model. The ensemble machine learning algorithms include Adaboost, Random-Forest, Bagging, Extremely Randomized Trees, Gradient Boosting, and Extra Trees Regressor. With the progress of open...

COLUMBIA UNIVERSITY HIGH SCHOOL SCIENCE HONORS PROGRAM 2007Apr 14 Sat Week 10 1. Machine Learning Intro Wikipedia: As a broad subfield of artificial intelligence, machine learning is concerned with the development of algorithms and techniques that allow computers to "learn". Most of machine learning works on extracting rules and patterns out of massive data sets. Some parts of machine learning ...

In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, an...

artificial immune systems (ais) can be defined as soft computing systems inspired by immune system of vertebrates. immune system is an adaptive pattern recognition system. ais have been used in pattern recognition, machine learning, optimization and clustering. feature reduction refers to the problem of selecting those input features that are most predictive of a given outcome; a problem encoun...

Lifelong machine learning (or lifelong learning) is an advanced machine learning paradigm that learns continuously, accumulates the knowledge learned in previous tasks, and uses it to help future learning. In the process, the learner becomes more and more knowledgeable and effective at learning. This learning ability is one of the hallmarks of human intelligence. However, the current dominant m...

introduction: manipulation of protein stability is important for understanding the principles that govern protein thermostability, both in basic research and industrial applications. various data mining techniques exist for prediction of thermostable proteins. furthermore, ann methods have attracted significant attention for prediction of thermostability, because they constitute an appropriate ...