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 present an automated solution to the classification problem of image categorization of images. Our system performs a series of experiments in an attempt to learn image categorization models from raw images, and the task is presented as an autonomous learning process using a computer vision system. Experiments show that the proposed method performs significantly better than prior methods in terms of classification accuracy, as well as in terms of image classification accuracy, which is demonstrated on our dataset, as well as on a new dataset, Flickr-2.3M, where it is compared to the state of the art methods on classification accuracy and image classification accuracy.

On the Transfer of Depth-Normal Sparse Representation for Efficient Object Detection

Neural Architectures of Visual Attention

Adaptive Stochastic Learning

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  • Convolutional neural networks and molecular trees for the detection of choline-ribose type transfer learning neurons

    Online Optimization for Neural Network TrainingWe present an automated solution to the classification problem of image categorization of images. Our system performs a series of experiments in an attempt to learn image categorization models from raw images, and the task is presented as an autonomous learning process using a computer vision system. Experiments show that the proposed method performs significantly better than prior methods in terms of classification accuracy, as well as in terms of image classification accuracy, which is demonstrated on our dataset, as well as on a new dataset, Flickr-2.3M, where it is compared to the state of the art methods on classification accuracy and image classification accuracy.


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