Ensemble learning is a technique to buildmachine learning applicationsusing multiple ML models instead of a single model.Here, an ensemble consists of various machine-learning models that participate in deciding the output of the machine-learning application. Each model in the ensemble is calibrated to...
Nian, "Ensembling extreme learning machine," Lecture Notes in Computer Science, vol. 4491, pp. 1069-1076, 2007.H. Chen, H. Chen, X. Nian, P. Liu, Ensembling extreme learning machines, in: Advances in Neural Networks, Lecture Notes in Computer Science, vol. 4491, Springer, Berlin, ...
It is usually desirable that the level 0 generalizers are of all “types”, and not just simple variations of one another (e.g., we want surface-fitters, Turing-machine builders, statistical extrapolators, etc., etc.). In this way all possible ways of examining the learning set and tryi...
machine-learning deep-neural-networks regression ensembling Updated May 25, 2022 Jupyter Notebook karthik-d / active-learning-boilerplate Star 1 Code Issues Pull requests Templated boilerplate for experiments in Active and Ensemble Learning. python template boilerplate machine-learning deep-learning ...
Recently, SSL has gained considerable attention when applied to deep learning-based methods, which are well known for their high demand on sizable and reliable labeled samples. Through distilling knowledge from unlabeled data, SSL boosts the deep learning-based methods to a new level in many tasks...
Deep learning approaches, on the other hand, overcome the issues in machine learning approaches. They are competent to learn automatically from primitive features without any domain knowledge28. Based on the employed features, deep learning methods can be categorized into i) structure-based, ii) seq...
From the result shown as below, with just 50,000 records in the data, using cuML for the Logistic Regression and SVC estimators in the VotingClassifier provides a 100x speedup. cuML's algorithms scale more effectively than their CPU equivalents because of the GPU's massive parallelism, high-ba...
Fuzzy neural networks are a powerful machine learning technique, that can be used in a large number of applications. Proper learning of fuzzy neural networ... A Natekin,A Knoll - International Conference on Engineering Applications of Neural Networks 被引量: 3发表: 2013年 Deep neural networks ...
Fig. 1. Distributed learning circumvents the need to share data to train a machine learning model. (A) In central learning, data is collected from a number of institutions into a centralized database. The central model is trained on this centralized database. (B) In distributed learning, mo...
Using FGE we can train high-performing ensembles in the time required to train a single model. We achieve improved performance compared to the recent state-of-the-art Snapshot Ensembles, on CIFAR-10, CIFAR-100, and ImageNet. 展开 关键词: Statistics - Machine Learning DOI: 10.48550/arXiv....