Systems, methods, and computer-readable storage media for aggregating the outputs of multiple machine learning models, then using the output of yet another machine learning model as a multiplier to obtain a final prediction. A system can receiving a plurality of data sets, each data set being ...
Analyzes 48 novel hybrid machine-learning prediction models. • Explores the impacts of CEEMDAN and VMD methods. • Implements and compares 4 original ANN structures. • Demonstrates the effect of genetic algorithm optimization. Abstract
Using Ensemble Machine Learning Methods to Forecast Particulate Matter (PM2.5) in Bangkok, Thailand Chapter © 2022 Explore related subjects Discover the latest articles, news and stories from top researchers in related subjects. Environmental Chemistry Availability of data and material The datasets...
aSix methods of measuring pKa were used for the 7912 DataWarrior chemicals. Only four chemicals had pKas measured by NMR, and five chemicals had kinetic measurements of pKa, thus those bars are not visible in the histogram. No information on the experimental method used to determine pKa was pr...
Single-task learning is the process of learning to predict a single outcome (binary, multi-class, or continuous) from a labeled data set. By contrast, multi-task learning is the process of jointly…
We discuss several nonparametric methods for attaching a standard error to a point estimate: the jackknife, the bootstrap, half-sampling, subsampling, bala... B Efron - 《Biometrika》 被引量: 1956发表: 1981年 Efficient noise-tolerant learning from statistical queries In this paper, we study the...
Domain Transfer Multiple Kernel Learning Cross-domain learning methods have shown promising results by leveraging labeled patterns from the auxiliary domain to learn a robust classifier for the ta... L Duan,IW Tsang,D Xu - 《IEEE Transactions on Pattern Analysis & Machine Intelligence》 被引量: ...
4.1 Multiple Kernel Learning The advent of kernel methods has provided a great deal of convenience in dealing with non-linear problems. Classification methods based on kernel theory have also had greater success in classifying features in HSI remote sensing images. Given a training data set with N...
Recently, the so-called multiple kernel learning methods have attracted considerable attention in the machine learning literature. In this paper, multiple kernel learning methods are shown to be specific cases of kernel machines with two layers in which the second layer is linear. Finally, a simple...
摘要: Many kernel based methods for multi-task learning have been proposed, which leverage relations among tasks to enhance the overall learning accuracies. Most of the methods assume that the learning tasks share the same kernel [eg, 13], which could limit...