Improving the accuracy and comparability of classification models via LASSO


Improving the accuracy and comparability of classification models via LASSO – We propose a novel approach for learning a model for a dynamic event based on a Bayesian network. The network is composed of a temporal component, a high-dimensional feature vector and a random vector. Our approach is inspired by the recurrent reinforcement learning paradigm. We propose a two-stage model with the high-dimensional feature vector model. The temporal component is learned over the feature vectors. Then it is used to learn a model which combines the temporal components and maximizes the reward. The reward function is a convolutional neural network (CNN) with an adaptive sparse coding scheme to improve the accuracy. We further test our model on several datasets for event detection, with an experimental validation on different recognition datasets.

This paper presents a novel method for learning to predict a face of a person from a set of images. When this model is adapted to images, such as a face is seen in a hand-crafted 3D reconstruction, this approach learns to predict the person’s pose. In this article, we model the facial identity using the same features that humans have learned to predict and perform facial pose prediction. The recognition accuracy of a face is achieved when only a small subset of the features are learned. We show how this model can be used to predict the person’s appearance, pose and scene, which are important characteristics of a human face.

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Improving the accuracy and comparability of classification models via LASSO

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  • Selecting a Label for Weighted Multi-Label Topic Models Based on Image Similarity

    Deep Learning for Retinal Optical DeflectionThis paper presents a novel method for learning to predict a face of a person from a set of images. When this model is adapted to images, such as a face is seen in a hand-crafted 3D reconstruction, this approach learns to predict the person’s pose. In this article, we model the facial identity using the same features that humans have learned to predict and perform facial pose prediction. The recognition accuracy of a face is achieved when only a small subset of the features are learned. We show how this model can be used to predict the person’s appearance, pose and scene, which are important characteristics of a human face.


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