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EnsembleSVM is a free software package containing efficient routines to perform ensemble learning with support vector machine (SVM) base models. It currently offers ensemble methods based on binary SVM models. Our implementation avoids duplicate storage and evaluation of support vectors which are shared between constituent models. Experimental results show(More)
We present an approximation scheme for support vector machine models that use an RBF kernel. A second-order Maclaurin series approximation is used for exponentials of inner products between support vectors and test instances. The approximation is applicable to all kernel methods featuring sums of kernel evaluations and makes no assumptions regarding data(More)
Optunity is a free software package dedicated to hyperparameter optimization. It contains various types of solvers, ranging from undirected methods to direct search, particle swarm and evolutionary optimization. The design focuses on ease of use, flexibility, code clarity and interoperability with existing software in all machine learning environments.(More)
Readily hydrolysable basic and dibasic esters of ampicillin were synthesised by alkylation of the carboxylate function of ampicillin to obtain prodrugs that may accumulate in cells and allow for an intracellular delivery of ampicillian (Fan et al., Bioorg. Med. Chem. Lett. 1997, 7, 3107). We found that the beta-lactam ring cleavage and the hydrolysis of the(More)
Assessing the performance of a learned model is a crucial part of machine learning. Most evaluation metrics can only be computed with labeled data. Unfortunately, in many domains we have many more unlabeled than labeled examples. Furthermore, in some domains only positive and unlabeled examples are available, in which case most standard metrics cannot be(More)
We present a novel approach to learn binary classifiers when only positive and unlabeled instances are available (PU learning). This problem is routinely cast as a supervised task with label noise in the negative set. We use an ensemble of SVM models trained on bootstrap subsamples of the training data for increased robustness against label noise. The(More)
CONTEXT In today's information-oriented society, data is abundantly available. Currently, analyzing the tsunami of information we dispose of often forms a bottleneck.In many practical data sets, several classes of data are available and the data mining goal is to distinguish these classes. If labeled data is available, a supervised learning technique called(More)
Early diagnosis is important for type 2 diabetes (T2D) to improve patient prognosis, prevent complications and reduce long-term treatment costs. We present a novel risk profiling approach based exclusively on health expenditure data that is available to Belgian mutual health insurers. We used expenditure data related to drug purchases and medical provisions(More)
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