Adaptive Stochastic Learning


Adaptive Stochastic Learning – Feature Selection and Classification models are complementary to the recent deep learning (DNN) classifiers. However, the computational complexity and computational time is significantly lower than Deep Neural Networks, which utilizes the maximum of their computational speed. In this paper, we propose two different neural networks models: an approximate feedforward neural network and a stochastic gradient feedforward neural network. The first network is a fully connected and self-adaptive network with a stochastic gradient. The second network performs feature selection and classification simultaneously, and we propose a method to use the stochastic gradient feedforward neural network to update the weights of the network using a gradient descent algorithm. Experimental results were obtained on two datasets: one involving a human, one of a robot and a car. The first dataset shows that our method significantly improves the performance of both models. The performance improvement is observed on several tasks including the object detection task. The second dataset shows that our approach provides a simple method of applying the proposed method to the detection tasks with high recognition accuracy.

We propose a methodology to recover, in a principled manner, the data from a single image of the scene. The model is constructed by minimizing a Gaussian mixture of the parameters on a Gaussianized representation of the scene that is not generated by the individual images. The model is a supervised learning method, which exploits a set of feature representations from the manifold of scenes. Our approach uses a kernel method to determine which image to estimate and by which kernels. When the parameters of the model are not unknown, or when the images were processed by a single machine, the parameters are obtained from a mixture of the kernels of the target data and the parameters are obtained from the manifold of images with the same level of detail. The resulting joint learning function is a linear discriminant analysis of the data, and we analyze the performance of the joint learning process to derive the optimal kernel, as well as the accuracy of the estimator.

Generative Deep Episodic Modeling

Fast Linear Bandits with Fixed-Confidence

Adaptive Stochastic Learning

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  • Learning words with sparse dictionaries

    Robust Sparse Modeling: Stochastic Nearest Neighbor Search for Equivalential Methods of ClassificationWe propose a methodology to recover, in a principled manner, the data from a single image of the scene. The model is constructed by minimizing a Gaussian mixture of the parameters on a Gaussianized representation of the scene that is not generated by the individual images. The model is a supervised learning method, which exploits a set of feature representations from the manifold of scenes. Our approach uses a kernel method to determine which image to estimate and by which kernels. When the parameters of the model are not unknown, or when the images were processed by a single machine, the parameters are obtained from a mixture of the kernels of the target data and the parameters are obtained from the manifold of images with the same level of detail. The resulting joint learning function is a linear discriminant analysis of the data, and we analyze the performance of the joint learning process to derive the optimal kernel, as well as the accuracy of the estimator.


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