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https://www.researchgate.net/publication/2461310_Model_Selection_for_Support_Vector_Machines
Model Selection for Support Vector Machines. ... may have large variance due to the limited size of learning sample. Several methods for model selection in SVM and SVM-like models were proposed , ...
https://www.ncbi.nlm.nih.gov/pubmed/24807923/
In particular, when the number of samples is small compared to their dimensionality, like in classification of microarray data, our proposal can outperform conventional out-of-sample approaches such as the cross validation, the leave-one-out, or the Bootstrap methods. PMID: 24807923 [PubMed]Cited by: 109
https://en.wikipedia.org/wiki/Support_vector_machine
The soft-margin support vector machine described above is an example of an empirical risk minimization (ERM) algorithm for the hinge loss. Seen this way, support vector machines belong to a natural class of algorithms for statistical inference, and many of its unique features are due to …
https://www.youtube.com/watch?v=N1vOgolbjSc
Aug 21, 2017 · SVMs are a popular classification technique used in data science and machine learning. In this video, I walk through how support vector machines work in a visual way, and then go step by step ...Author: Alice Zhao
http://www.cenparmi.concordia.ca/ICFHR2008/Proceedings/papers/cr1099.pdf
enough to be learned with only one training or test sample. 3. Model selection with Support Vector Machines First, we discuss the principle of our approach, then we present kernels for generative models. These kernels are used to select component models in a mixture model defined according to Eq. (1). Finally we discuss the
https://scikit-learn.org/stable/modules/svm.html
The support vector machines in scikit-learn support both dense (numpy.ndarray and convertible to that by numpy.asarray) and sparse (any scipy.sparse) sample vectors as input. However, to use an SVM to make predictions for sparse data, it must have been fit on such data.
https://www.sciencedirect.com/science/article/pii/S0925231203003758
We address the problem of model selection for Support Vector Machine (SVM) classification. For fixed functional form of the kernel, model selection amounts to tuning kernel parameters and the slack penalty coefficient C.We begin by reviewing a recently developed probabilistic framework for SVM classification.Cited by: 209
https://ieeexplore.ieee.org/document/6228541/
The experiments, performed both on simulated and real-world datasets, show that our in-sample approach can be favorably compared to out-of-sample methods, especially in cases where the latter ones provide questionable results.Cited by: 109
https://www.academia.edu/13532662/In-sample_model_selection_for_Support_Vector_Machines
In-sample model selection for Support Vector Machines
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