[ad_1] Introduction: Gaze estimation for attention tracking has become a prominent research area in recent years, with applications in various fields such as human-computer interaction, psychology, and medical diagnosis. The ability to accurately estimate where…
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[ad_1] Introduction In recent years, there has been a growing interest in the development and application of deep learning techniques for the analysis of spatial data. Convolutional Deep Belief Networks (CDBNs) have emerged as a…
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[ad_1] Introduction Self-organizing maps (SOMs) have been widely used in various fields such as machine learning, data visualization, pattern recognition, and clustering. One of the key advantages of SOMs is their ability to preserve the…
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[ad_1] Introduction Autoencoders have gained significant attention in recent years as powerful tools for unsupervised representation learning. These neural networks are capable of learning compact and meaningful representations of data without the need for labeled…
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[ad_1] Introduction Artificial Neural Networks (ANNs) have become a popular tool in the field of machine learning and artificial intelligence due to their ability to approximate complex functions. In recent years, ANNs have been used…
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[ad_1] **Introduction** Gradient boosting machines (GBM) have become a popular machine learning technique for building predictive models in various fields such as finance, healthcare, and marketing. GBM is a powerful ensemble learning method that combines…
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[ad_1] Introduction Random forests are a powerful ensemble learning method that has gained popularity in various fields of study, including machine learning, data mining, and bioinformatics. This thesis focuses on understanding the principles behind random…
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[ad_1] Introduction Support vector machines (SVM) are powerful machine learning algorithms that have gained popularity in recent years due to their ability to handle both classification and regression tasks effectively. SVM works by finding the…
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[ad_1] Introduction Hidden Markov models (HMMs) are powerful statistical models used in a wide range of applications, including speech recognition, bioinformatics, and natural language processing. In recent years, they have become increasingly popular for sequence…
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[ad_1] Introduction Markov decision processes (MDPs) are a powerful framework for modeling sequential decision-making problems in which an agent interacts with a dynamic environment. MDPs have been widely used in a variety of fields, including…
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