Multiple-instance learning consists of two alternating optimization steps: learning a classifier with missing labels and finding the missing labels with the classifier. These steps are iteratively performed on the same training data, thus imputing labels by evaluating the classifier on the data it is ...
The RSIS method allows to design MIL ensembles that are robust to various witness rate, because each time one of the classifiers in the ensemble is trained, only one instance is used from each bag. The instances are drawn based on their probability of being positive. If the witness rate is...
Prati, F. Herrera, A first study on the use of noise filtering to clean the bags in multi-instance classification, in: Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications, LOPAL ’18, Association for Computing Machinery, New York, NY, USA...
For instance, we have utilized scCube to generate a series of simulated spot-based SRT data with different resolutions but the same other spatial variability (such as the number, proportion and spatial distribution of cell types) and benchmarked nine widely used spot deconvolution methods. ...
Multiple instance learning (MIL) is considered a generalization of traditional supervised learning which deals with uncertainty in the information. Together with the fact that, as in any other learning framework, the classifier performance evaluation maintains a trade-off relationship between different ...
Instance Selection) [5]. It maps each bag to a feature space defined by all the instances in the training bags, and then performs joint feature selection and classification by using the 1-norm SVM [15]. Empirically, it is highly efficient, accurate and robust to label noise. Typi...
Multiple Instance Learning toolbox for Matlab. Contribute to HappyCoderGS/mil development by creating an account on GitHub.
, we observed that in our instance vinylboronic acid performed comparatively much better as a catalyst, while aliphatic boronic acids 22 and 23 were deleterious in the reaction. Density functional theory (DFT) studies (Supplementary Section ‘Computational Details’) suggested that fine-tuning electron...
Classic MIL formulation can lead to gradient vanishing problems when training a multiple instance neural network. To this end, log-likelihood function is recently utilized for optimization [9]. Here, bag label is distributed as a Bernoulli distribution with the parameter \(\theta (X)\in [0,1]...
For instance, the weight distribution of various indicators should be different in emerging and developed countries. Therefore, the purpose of this paper is to construct various scenarios of QOLI using linguistic quantifiers of the ordered weighted averaging (OWA) operator, and the 2-tuple linguistic...