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Gesture recognition for human-computer interaction – Complete Phd and Masters Thesis

Gesture recognition for human-computer interaction – Complete Phd and Masters Thesis

[ad_1] Introduction: Gesture recognition is a technology that allows a computer to interpret human gestures as commands for controlling devices or interacting with software applications. It has gained popularity as a natural and intuitive way…

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Gaze estimation for attention tracking – Complete Phd and Masters Thesis

Gaze estimation for attention tracking – Complete Phd and Masters Thesis

[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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Video summarization for highlight extraction – Complete Phd and Masters Thesis

Video summarization for highlight extraction – Complete Phd and Masters Thesis

[ad_1] Introduction In recent years, the explosion of online video content has created a need for automated methods of video summarization for highlight extraction. Video summarization involves condensing the content of a video into a…

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Convolutional deep belief networks for spatial data – Complete Phd and Masters Thesis

Convolutional deep belief networks for spatial data – Complete Phd and Masters Thesis

[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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Hopfield networks for associative memory – Complete Phd and Masters Thesis

Hopfield networks for associative memory – Complete Phd and Masters Thesis

[ad_1] Introduction Hopfield networks are a type of recurrent neural network that have been widely used for associative memory tasks. First introduced by John Hopfield in 1982, these networks are capable of storing and recalling…

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Self-organizing maps for topology preservation – Complete Phd and Masters Thesis

Self-organizing maps for topology preservation – Complete Phd and Masters Thesis

[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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Gradient boosting machines for additive models – Complete Phd and Masters Thesis

Gradient boosting machines for additive models – Complete Phd and Masters Thesis

[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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Decision trees for interpretable models – Complete Phd and Masters Thesis

Decision trees for interpretable models – Complete Phd and Masters Thesis

[ad_1] Introduction: Decision trees are a popular and widely used machine learning technique that provides an interpretable model for making decisions. This thesis explores the use of decision trees for developing interpretable models, focusing on…

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Gaussian processes for function approximation – Complete Phd and Masters Thesis

Gaussian processes for function approximation – Complete Phd and Masters Thesis

[ad_1] Introduction: Gaussian processes are a powerful tool in machine learning for function approximation. They offer a flexible framework for modeling complex, non-linear relationships in data, while also providing uncertainty estimates for predictions. In recent…

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Kalman filters for state estimation – Complete Phd and Masters Thesis

Kalman filters for state estimation – Complete Phd and Masters Thesis

[ad_1] Introduction Kalman filters are a powerful tool in the field of state estimation, allowing for the estimation of the true state of a system based on noisy measurements. Originally developed by Rudolf E. Kalman…

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