A neural network method using the Rayleigh quotient with Gram-Schmidt orthogonalization solves differential eigenvalue problems in order, including parametric, nonlinear, and high-dimensional cases.
Image Classification with Classic and Deep Learning Techniques
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
To classify images based on their content is one of the most studied topics in the field of computer vision. Nowadays, this problem can be addressed using modern techniques such as Convolutional Neural Networks (CNN), but over the years different classical methods have been developed. In this report, we implement an image classifier using both classic computer vision and deep learning techniques. Specifically, we study the performance of a Bag of Visual Words classifier using Support Vector Machines, a Multilayer Perceptron, an existing architecture named InceptionV3 and our own CNN, TinyNet, designed from scratch. We evaluate each of the cases in terms of accuracy and loss, and we obtain results that vary between 0.6 and 0.96 depending on the model and configuration used.
citation-role summary
other 1
citation-polarity summary
fields
math.NA 1years
2025 1verdicts
CONDITIONAL 1roles
other 1polarities
unclear 1representative citing papers
citing papers explorer
-
Solving engineering eigenvalue problems with neural networks using the Rayleigh quotient
A neural network method using the Rayleigh quotient with Gram-Schmidt orthogonalization solves differential eigenvalue problems in order, including parametric, nonlinear, and high-dimensional cases.