Applying gradient boosting as a stac king algorithm over bottleneck features to achieve high image classification accuracy - Golian, N.; Afanasieva, I.; Golian, V.; Panchenko, D. (ХНУРЭ, 2021)
With the development of the Internet, making many images available online for analysis, object recognition software is gaining more and more attention from researchers. Factors are driving the development of computer vision today: mobile devices with built-in cameras, the availability of computing power, the availability of computer vision and analysis equipment, and new algorithms such as convolutional neural networks that take advantage of the power of hardware and software. The work is generally devoted to the consideration of the problem of image classification using convolutional neural networks. And in particular, one of the most popular and applied in practice machine learning algorithms − gradient boosting applied to the bottlenecks of deep convolutional neural networks. It also discusses three scenarios for applying gradient boosting to bottlenecks extracted from the last convolutional layer of the neural network. The essence of boosting, as well as of other ensembles of algorithms, is to collect one strong from several weak models. The general idea of boosting algorithms is to consistently apply predictors so that each subsequent model minimizes the error of the previous one. Gradient boosting works by sequentially adding new models to past models so that errors made by previous predictors are corrected.
Keywords: artificial intelligence; computer vision; gradient boosting; image; machine learning; neural network