In this paper tuning for deep learning algorithms is performed for face alignment and pose estimation problems. For pose estimation the classical indirect method (from fp68 landmarks via Candide model to pose) is comp...
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With the explosion of "Big Data", the application of statistical learning models has become popular in multiple scientific areas as well as in marketing, finance or other business disciplines. Nonetheless, t...
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When entering the society, with the rapid development of computer technology, people have higher and deeper requirements for information transmission. Therefore, in the face of constantly updated and changing knowledg...
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Using recent machine learning results that present an information-theoretic perspective on underfitting and overfitting, we prove that deciding whether an encodable learning algorithm will always underfit a dataset, e...
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The purpose of this paper is to present a new learning algorithm based on an adaptive multi-agent system and to compare it with classical learning algorithms such as the Multi-Layer Perceptron (MLP), the Support Vecto...
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Backpropagation (BP) learning algorithm is the most widely supervised learning technique which is extensively applied in the training of multi-layer feed-forward neural networks. Many modifications of BP have been pro...
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Transformers have demonstrated effectiveness in in-context solving data-fitting problems from various (latent) models, as reported by Garg et al. (2022). However, the absence of an inherent iterative structure in the ...
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Precancerous conditions such as intestinal metaplasia (IM) have a key role in gastric cancer development and can be detected during endoscopy. During upper gastrointestinal endoscopy (UGIE), misdiagnosis can occur due...
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Information era, piracy converted a pivotal issue for the survival of the movie industry. Because of freebooting, the cinema industry suffers a substantial quantity of financial lose. The important task is to govern t...
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This paper revisits the constrained learning algorithm (CLA) proposed by Perantonis & Karras, and makes further analyses and discussions on the parameters with the CLA's. Specifically, we investigate the effec...
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This paper revisits the constrained learning algorithm (CLA) proposed by Perantonis & Karras, and makes further analyses and discussions on the parameters with the CLA's. Specifically, we investigate the effect of removing the constrained condition of the weight change on the CLA's. It is suggested that for those problems that do not need to do precise computation, the modified CLA is a better choice. Finally, some simulation results are presented to support our claims.
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