Results 281 to 290 of about 1,011,171 (338)

SISTEM KLASIFIKASI JENIS KERANG BERDASARKAN CITRA CANGKANG MENGGUNAKAN SUPPORT VECTOR MACHINE (SVM)

Adinda, Seffi Rozahana, Nadia Ayu Putri Priyani, Apriliani Putri, Irsyad Widiansyah, Nurul Hayaty   +5 more
openalex   +2 more sources

Prediction Models for Late-Onset Preeclampsia: A Study Based on Logistic Regression, Support Vector Machine, and Extreme Gradient Boosting Models. [PDF]

Biomedicines
Zhang Y, Gu X, Yang N, Xue Y, Ma L, Wang Y, Zhang H, Jia K.   +7 more
europepmc   +1 more source

Experimental Validation and Support Vector Machine Optimization of Rice Husk Gasification for Sustainable Syngas Production and Dual-Fuel Engine Application

Deepak Kumar Murugan, Zafar Said, Dineshbabu D, S. Shankaranarayanan, Gopinath Dhamodaran, C V Dayakar   +5 more
openalex   +1 more source

Genetic Artificial Hummingbird Algorithm-Support Vector Machine for Timely Power Theft Detection. [PDF]

ScientificWorldJournal
Gbafore E, Segera DR, Kiruki CRM.
europepmc   +1 more source

Analisis Sentimen Pengguna terhadap Aplikasi Lalamove dengan Perbandingan Algoritma Support Vector Machine dan Naive Bayes

Dhea Nurajizah, Shofa Shofia Hilabi, Agustia Hananto, Baenil Huda   +3 more
openalex   +2 more sources

Efficient and Accurate Brain Tumor Classification Using Hybrid MobileNetV2-Support Vector Machine for Magnetic Resonance Imaging Diagnostics in Neoplasms. [PDF]

Brain Sci
Adamu MJ, Kawuwa HB, Qiang L, Nyatega CO, Younis A, Fahad M, Dauya SS.   +6 more
europepmc   +1 more source

Comparative Analysis of Support Vector Machine, Random Forest and k-Nearest Neighbor Classifiers for Predicting Remaining Usage Life of Roller Bearings

Rajkumar Palaniappan
openalex   +2 more sources

Some of the next articles are maybe not open access.

Related searches:

Distributed Support Vector Machines

IEEE Transactions on Neural Networks, 2006
A truly distributed (as opposed to parallelized) support vector machine (SVM) algorithm is presented. Training data are assumed to come from the same distribution and are locally stored in a number of different locations with processing capabilities (nodes).
A, Navia-Vazquez, D, Gutierrez-Gonzalez, E, Parrado-Hernandez, J J, Navarro-Abellan   +3 more
openaire   +2 more sources

Field support vector machines

Proceedings of the 1st International Conference on Internet of Things and Machine Learning, 2017
Conventional classifiers often regard input samples as identically and independently distributed (i.i.d.). This is however not true in many real applications, especially when patterns occur as groups (where each group shares a homogeneous style). Such tasks are also called field classification. By breaking the i.i.d.
Kaizhu Huang, Haochuan Jiang, Xu-Yao Zhang   +2 more
openaire   +1 more source