These Are The Most Elegant, Useful Algorithms In Machine Learning

#artificialintelligence

Developed back in the 50s by Rosenblatt and colleagues, this extremely simple algorithm can be viewed as the foundation for some of the most successful classifiers today, including suport vector machines and logistic regression, solved using stochastic gradient descent. The convergence proof for the Perceptron algorithm is one of the most elegant pieces of math I've seen in ML. Most useful: Boosting, especially boosted decision trees. This intuitive approach allows you to build highly accurate ML models, by combining many simple ones. Boosting is one of the most practical methods in ML, it's widely used in industry, can handle a wide variety of data types, and can be implemented at scale.



A Brief History of Machine Learning - DATAVERSITY

#artificialintelligence

Machine Learning (ML) is an important aspect of modern business and research. It uses algorithms and neural network models to assist computer systems in progressively improving their performance. Machine Learning algorithms automatically build a mathematical model using sample data – also known as "training data" – to make decisions without being specifically programmed to make those decisions. Machine Learning is, in part, based on a model of brain cell interaction. The model was created in 1949 by Donald Hebb in a book titled The Organization of Behavior (PDF).


Tensor Sparse PCA and Face Recognition: A Novel Approach

arXiv.org Machine Learning

Face recognition is the important field in machine learning and pattern recognition research area. It has a lot of applications in military, finance, public security, to name a few. In this paper, the combination of the tensor sparse PCA with the nearest-neighbor method (and with the kernel ridge regression method) will be proposed and applied to the face dataset. Experimental results show that the combination of the tensor sparse PCA with any classification system does not always reach the best accuracy performance measures. However, the accuracy of the combination of the sparse PCA method and one specific classification system is always better than the accuracy of the combination of the PCA method and one specific classification system and is always better than the accuracy of the classification system itself.


Artificial Intelligence Approaches to Problems in the Human Genome Project

AAAI Conferences

Problems of protein structure Our research focuses on two important problems in molecular biology, both critical components of the Human Genome Project. The first problem is that of protein folding, where our emphasis is on prediction of secondary structure from primary sequence data. The second problem is identification of the motifs, or building blocks, from which all proteins are constructed. Below we summarize our past results and current approaches to these problems. Protein folding We consider two approaches to protein folding.