After labeling, supervised machine learning algorithms of k-Nearest Neighbors (kNN), Support Vector Machine (SVM) and Random Forest (RF) are applied to obtain the trained models for future classification. The kN
Clustering is an approach to unsupervised learning. There is no labeling required, unlike classification tasks. In broad terms, clustering can be expressed as exploring the unknown. The wide range of clustering applications includes search engines, social networks, visual tasks such as image segmentatio...
clusters, albeit with higher prevalence in the clusters associated with distributed learning. These results provide an improved characterisation as compared to common machine-learning classification algorithms trained on two statistical measures from the time-series. The analysis could be enhanced by the us...
S6a). More importantly, the positive prediction values (PPV) of GIANA reached over 60% for all epitopes, while the PPVs of GLIPH2 for 2 out of the 3 epitopes were lower than 20% (Fig. S6b). Ultra-fast sample query and TCR repertoire classification The high speed and specificity of ...
Smile (Statistical Machine Intelligence and Learning Engine) is a fast and comprehensive machine learning system. With advanced data structures and algorithms, Smile delivers the state-of-art performance. Smile covers every aspect of machine learning, including classification, regression, clustering, associ...
If your training data has labels, consider using one of the supervisedclassificationmethods provided in Machine Learning. For example, you might compare the results of clustering to the results when using one of the multiclass decision tree algorithms. ...
If your training data has labels, consider using one of the supervisedclassificationmethods provided in Machine Learning. For example, you might compare the results of clustering to the results when using one of the multiclass decision tree algorithms. ...
http://papers.nips.cc/paper/7001-learning-a-structured-optimal-bipartite-graph-for-co-clustering.pdf Peng X, Xu D. Structural regularized projection twin support vector machine for data classification. Information Sciences. 2014;279(279):416–32. Article Google Scholar Prats-Montalbn JM, Lopez ...
Sattar H, Ying Y, Zahra M, Mohammadreza K (2009) Adapted one-vs-all decision trees for data stream classification. IEEE Trans Knowl Data Eng 21:624–637 Article Google Scholar Sebastiani F (2002) Machine learning in automated text categorization. ACM Comput Surv (CSUR) 34(1):1–47 Arti...
A Python toolkit/library for reality-centric machine/deep learning and data mining on partially-observed time series, including SOTA neural network models for scientific analysis tasks of imputation/classification/clustering/forecasting/anomaly detection