Survey on SVM and their application in image classification
By Mayank Arya Chandra, Sarabjeet Singh Bedi. International journal of information technology, 2018
595 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Media Technology
View Paper: https://www.semanticscholar.org/paper/95e2840794e52b7d909c246aadae380889094f99
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- D. M. Abdullah
- Aristotelis C. Tagarakis
- Lefteris Benos
- Georgios Dolias
- Remigio Berruto
- Dimitrios Kateris
- Dionysis D. Bochtis
- Adnan Mohsin Abdulazeez
Citations in the corpus
- Machine Learning Applications based on SVM Classification A Review (2021, background): “However, for all types of data set, no single classification approach has been found to provide the best accuracy [1,2].”
- Machine Learning in Agriculture: A Comprehensive Updated Review (2021, background): “SVM completes the group with the three most accurate ML models in agriculture, due to some advantages, such as its high performance when it works with image data [101].”
- A review of convolutional neural networks in computer vision (2024, unknown): “Additionally, SPP-Net ignores the parameters of the network model’s other layers and only adjusts the fully connected layer.”