Support vector machines (SVMs) can be trained to be very accurate classifiers and have been used in many applications. However, the training time and, to a lesser extent, prediction time of SVMs on very large data sets can be very long. This paper presents a fast compression method to scale up SVMs to large data sets. A simple bit-reduction method is applied to reduce the cardinality of the data by weighting representative examples. We then develop SVMs trained on the weighted data. Experiments indicate that bit-reduction SVM produces a significant reduction in the time required for both training and prediction with minimum loss in accuracy. It is also shown to typically be more accurate than random sampling when the data are not overcompressed.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4467789 | PMC |
| http://dx.doi.org/10.1109/TSMCB.2008.2011645 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Comparative analysis of regression algorithms for drug response prediction using GDSC dataset.
BMC Res Notes
January 2025
Department of Computer Engineering, Chungbuk National University, Chungdae-ro 1, Cheongju, 28644, Republic of Korea.
Background: Drug response prediction can infer the relationship between an individual's genetic profile and a drug, which can be used to determine the choice of treatment for an individual patient. Prediction of drug response is recently being performed using machine learning technology. However, high-throughput sequencing data produces thousands of features per patient.
View Article and Find Full Text PDFSupervised machine learning statistical models for visual outcome prediction in macular hole surgery: a single-surgeon, standardized surgery study.
Int J Retina Vitreous
January 2025
Department of Retina and Vitreous, Narayana Nethralaya, #121/C, 1st R Block, Chord Road, Rajaji Nagar, Bengaluru, 560010, India.
Purpose: To evaluate the predictive accuracy of various machine learning (ML) statistical models in forecasting postoperative visual acuity (VA) outcomes following macular hole (MH) surgery using preoperative optical coherence tomography (OCT) parameters.
Methods: This retrospective study included 158 eyes (151 patients) with full-thickness MHs treated between 2017 and 2023 by the same surgeon and using the same intraoperative surgical technique. Data from electronic medical records and OCT scans were extracted, with OCT-derived qualitative and quantitative MH characteristics recorded.
Classification Prediction of Hydrocephalus After Intercerebral Haemorrhage Based on Machine Learning Approach.
Neuroinformatics
January 2025
Department of Mathematics and Statistics, Changsha University of Science and Technology, Changsha, 410114, China.
In order to construct a clinical classification prediction model for hydrocephalus after intercerebral haemorrhage(ICH) to guide clinical treatment decisions, this paper retrospectively analyses the clinical data of 844 cases of ICH and hydrocephalus inpatients admitted to Yueyang People's Hospital from May 2019 to October 2022, of which 95 cases of hydrocephalus occurred after ICH and no hydrocephalus in 749 cases. The following indicators were compared between the two groups of patients: gender, age, Glasgow Coma Scale(GCS)score, whether the amount of bleeding was greater than 30 ml, whether it broke into the ventricle or not, modified Graeb score(MGS), modified Rankin Scale (MRS) score, whether surgery was performed or not, red blood cells, white blood cells, and platelets. After variable screening, the following six variables were selected: GCS score, MGS, MRS score, whether the bleeding volume was greater than 30 ml, whether it broke into the ventricle or not, and whether surgery was performed or not were modelled and analysed using logistic regression model and support vector machine model in machine learning.
View Article and Find Full Text PDFmRNA vaccines with RBD mutations have broad-spectrum activity against SARS-CoV-2 variants in mice.
NPJ Vaccines
January 2025
Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical college, Kunming, China.
The emergence of SARS-CoV-2 variants with defined mutations that enhance pathogenicity or facilitate immune evasion has resulted in a continual decline in the protective efficacy of existing vaccines. Therefore, there is a pressing need for a vaccine capable of combating future variants. In this study, we designed new mRNA vaccines, BSCoV05 and BSCoV06, and generated point mutations in the receptor-binding domain (RBD) of the original Wuhan strain to increase their broad-spectrum antiviral activity.
View Article and Find Full Text PDFIntegrating machine learning and structure-based approaches for repurposing potent tyrosine protein kinase Src inhibitors to treat inflammatory disorders.
Sci Rep
January 2025
State Key Laboratory of Chemical Resources Engineering, Beijing University of Chemical Technology, Beijing, 100029, People's Republic of China.
Tyrosine-protein kinase Src plays a key role in cell proliferation and growth under favorable conditions, but its overexpression and genetic mutations can lead to the progression of various inflammatory diseases. Due to the specificity and selectivity problems of previously discovered inhibitors like dasatinib and bosutinib, we employed an integrated machine learning and structure-based drug repurposing strategy to find novel, targeted, and non-toxic Src kinase inhibitors. Different machine learning models including random forest (RF), k-nearest neighbors (K-NN), decision tree, and support vector machine (SVM), were trained using already available bioactivity data of Src kinase targeting compounds.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!