Summary of Classification with Neural Networks with Quadratic Decision Functions, by Leon Frischauf et al.
Classification with neural networks with quadratic decision functions
by Leon Frischauf, Otmar Scherzer, Cong Shi
First submitted to arxiv on: 19 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Numerical Analysis (math.NA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Neural networks with quadratic decision functions have been proposed as alternatives to standard neural networks with affine linear ones. These models are advantageous when identifying compact objects or classes of basic geometries like circles and ellipses. This paper investigates the use of these ansatz functions for classification, testing and comparing the algorithm on the MNIST dataset for handwritten digit recognition and subspecies classification. The implementation is also shown to be feasible using neural network structures in software frameworks such as TensorFlow and Keras. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Neural networks with special shapes can help identify objects that are close together or have simple shapes like circles. In this paper, researchers test these “quadratic decision function” models on a dataset of handwritten digits and animal subspecies to see if they work well for classification tasks. They also show how to implement these models using popular programming tools called TensorFlow and Keras. |
Keywords
* Artificial intelligence * Classification * Neural network
