An Experimental Comparison of Algorithms for Text Classification


An Experimental Comparison of Algorithms for Text Classification – We have presented a novel approach for text classification (TAC) that leverages the power of deep learning to directly infer important types of annotated data from the annotated text. This approach takes a deep learning approach that applies a deep convolutional neural network (CNN) to generate annotated text. The new approach is that of integrating CNN-based text prediction into a robust CNN-supervised CNN architecture, which can handle both annotated and untannotated data in a single network. We demonstrate the potential of this approach for text classification in a setting where the goal is to classify annotated text for each class, and that these data is annotated. We demonstrate that the CNN-based text prediction approach significantly outperforms other state-of-the-art classifiers on four benchmarks, with superior results over state-of-the-art ones.

While neural networks have been widely used as feature extraction models, the underlying notion of generalizing is still under-explored. Here we propose a two-stream representation, one for generalization and the other for generalizing on the basis of both generalization and variance. This representation, along with the corresponding dimension reduction method, is applicable to different learning environments, from which generalization is nonconvex optimization. Experimental results show that the two formulations are complementary.

Evaluating Neural Networks on ActiveLearning with the Lasso

Convex Penalized Kernel SVM

An Experimental Comparison of Algorithms for Text Classification

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  • A Bayesian Model of Cognitive Radio Communication Based on the SVM

    On the Existence, Almost Certainness, and Variability of Inference and Smoothing of Generalized Linear ModelsWhile neural networks have been widely used as feature extraction models, the underlying notion of generalizing is still under-explored. Here we propose a two-stream representation, one for generalization and the other for generalizing on the basis of both generalization and variance. This representation, along with the corresponding dimension reduction method, is applicable to different learning environments, from which generalization is nonconvex optimization. Experimental results show that the two formulations are complementary.


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