algorithm (GA), particle swarm size (PSO), and cuckoo search algorithm (COA) are combined with two machine-learning techniques namely multilayer perception (MLP) neural network and least square support vector m
Most methods basically treat machine learning algorithms as a final stop in data process. The residuals out of the models are usually not checked and guaranteed for whiteness. However, it is found that the traffic features still exist in the residuals. In this research, we propose a novel ...
If we did, we would use it directly and not need to learn it from data using machine learning algorithms.The most common type of machine learning is to learn the mapping Y = f(X) to make predictions of Y for new X. This is called predictive modeling or predictive analytics, and our ...
Neural networks constitute a different class of machine learning algorithms that are loosely inspired by the human brain. Organized as a directed graph of trainable units (neurons), neural networks can learn highly nonlinear relations between input data and output data. The machine learning models and...
The present study examines the role of feature selection methods in optimizing machine learning algorithms for predicting heart disease. The Cleveland Heart disease dataset with sixteen feature selection techniques in three categories of filter, wrapper,
Today, computer algorithms are emulating that capability in a set of technologies broadly called Machine Learning (ML). In ML, which is related to big data and data mining, large data sets are labeled by humans with the names of the objects within them: people, places, things, events, patt...
2. Machine Learning by Stanford University This Machine Learning course is taught by Andrew Ng, who was formerly Chief Scientist at Baidu and Director of Google Brain Deep Learning Project. It includes both theoretical and practical aspects of machine learning algorithms. Furthermore, you can learn...
We review two major approaches to inductive machine learning, rule algorithms and decision tree algorithms, by describing representative algorithms for both. Next, we describe an algorithm representing a family of hybrid algorithms combining the two approaches. In Appendix A6 we give a comprehensive ...
(AI), particularly,machine learning (ML)is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, thedeep learning, which is part of a broader family of machine learning methods, can ...
Hybrid techniques of machine learning algorithms obtain better results than the simple techniques studied in the literature. We also verified how the BAGNET method, with an average recall value of 93.41% and without falling below 90% in any of the estuaries, exceeds the results obtained by other...