IB2 Instance-Based Learning 2 CS Condensing set TS Training set MLCNN-H Multilabel Condensed Nearest Neighbor with Hamming Distance MLCNN-J Multilabel Condensed Nearest Neighbor with Jaccard Distance MLCNN-L Mu
Histopathologic Brain Age Estimation via Context‐aware Attention‐based Deep Multiple Instance LearningBackground While a large list of age-related changes have been described in the human brain, the extent to which they represent the result of common pathological reactions versus signatures of ...
Even among the regularized regression methods, increasing model complexity from simple through the adaptive to grouped or even the Bayesian regularized methods, generally only increased computing time without clearly improving predictive performance. The ensemble, instance-based and deep-learning ML methods ...
The size, velocity, and heterogeneity of Big Data outclasses conventional data management tools and requires data and metadata to be fully machine-actionable (i.e., eScience-compliant) and thus findable, accessible, interoperable, and reusable (FAIR). Th
Initially, the EQ format was used for characterizing and classifying different mutant phenotypes of a given model organism by comparing them to their canonical wild-type and then relating them to their underlying genotype [56,64,73,74]. The wild-type functioned as a ‘normal’ condition and poi...
[24] used a random forest regression model to predict the IRI of pavements based on the LTPP database. One of the primary limitations of machine learning models is the size of the dataset. In the above-mentioned studies, the machine learning models are all based on a large amount of road...
Deepweeds: a multiclass weed species image dataset for deep learning Sci. Rep., 9 (2019), p. 2058 View in ScopusGoogle Scholar [3] B. Melander, B. Lattanzi, E. Pannacci Intelligent versus non-intelligent mechanical intra-row weed control in transplanted onion and cabbage Crop. Prot., ...
McCallum A (1999) Multi-label text classification with a mixture model trained by EM. In: Working notes of the AAAI workshop on text learning, pp 1–7 Ray S, Craven M (2005) Supervised versus multiple instance learning: an empirical comparison. In: Proceedings of the 22nd IEEE ICDM intern...
Our model was designed according to the Instance-Based Learning Theory (IBLT). The two versions of the model assumed the same cognitive principles of decision making and learning in the MEC. The only difference between the two models was the assumption of homogeneity among the four participants:...
To further assess the MIPART method’s performance, the ROC area metric was evaluated across twelve multi-instance learning datasets, as indicated in Figure 6. The receiver operating characteristic (ROC) curve provides a graphical representation of the true positive rate (TPR) versus the false posi...