Towards Generalized Deep Learning Models for Classification


Towards Generalized Deep Learning Models for Classification – We explore the influence of class labels on the performance of different classifiers from a given dataset. We establish bounds for the effect of class labels on performance in the classification of multi-label datasets, which are the most common datasets in academia. In particular, we provide a new baseline for the influence of class labels on multi-label classification. Specifically, we develop a technique to evaluate the effect of class labels on prediction performance. This approach is inspired by the notion of importance in classification as a function of the number of labels in a single dataset. In our experiments, we demonstrate that classification accuracy for the classification of multi-label datasets is better than the ability of class labels to predict the same classification. We also show that our approach is more accurate than state-of-the-art classification methods in predicting the classification accuracy.

We present a simple CNN-based model-based image segmentation tool with powerful machine learning ability. The tool leverages both a deep learning model and an external image for training the CNN that performs a discriminative feature extraction to extract the fine-grained features from the images. We also use the tool to train our model into a feature extraction system in order to improve the performance of the CNN model while preserving the visual quality of the segmented image and reducing the computational burden for the tool. In this paper, we also provide extensive experiments on benchmark images that demonstrate the quality of our approach.

A Novel Architecture for Multi-level Classification on Sequential Data

Deep Network Trained by Combined Deep Network Feature and Deep Neural Network

Towards Generalized Deep Learning Models for Classification

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  • Robust Constraint Handling with Answer Set Programming

    Pulmonary Encephalitis Detection with Deep Convolutional Neural NetworksWe present a simple CNN-based model-based image segmentation tool with powerful machine learning ability. The tool leverages both a deep learning model and an external image for training the CNN that performs a discriminative feature extraction to extract the fine-grained features from the images. We also use the tool to train our model into a feature extraction system in order to improve the performance of the CNN model while preserving the visual quality of the segmented image and reducing the computational burden for the tool. In this paper, we also provide extensive experiments on benchmark images that demonstrate the quality of our approach.


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