Transfer learning can improve the performance of classifier effectively, when the training data are out of date, but the new data are very few. In this paper, we propose a transfer learning algorithm for text classification based on clustering. We describe the main idea and the step of the ...
With the advancement in deep learning, neural network architectures like recurrent neural networks (RNN and LSTM) and convolutional neural networks (CNN) have shown a decent improvement in performance in solving several Natural Language Processing (NLP) tasks like text classification, language mod...
[Triplex Transfer Learning: Exploiting both Shared and Distinct Concepts for Text Classification] (intsci.ac.cn/users/zhua), Fuzhen Zhuang , Ping Luo, et al.,Matlab [Heterogeneous Transfer Learning for Image Classification] (cse.ust.hk/~yinz/htl4ic), Yin Zhu, Yuqiang Chen, et al.,Matlab [...
For more information about how to use the new SageMaker TensorFlow text classification algorithm for transfer learning on a custom dataset, deploy the fine-tuned model, run inference on the deployed model, and deploy the pre-trained model as is without first fine-tuning on a cu...
Bridging Domains Using World Wide Knowledge for Transfer Learning Data miningWikipedia.cross-domaintext classificationtransfer learningA major problem of classification learning is the lack of ground-truth labeled data. It is... Xiang,E Wei - 《IEEE Transactions on Knowledge & Data Engineering》 被引...
Hybrid classical-quantum transfer learning for text classification Quantum machine learning (QML) is a promising field that combines the power of quantum computing with machine learning. Variational quantum circuits, where... E Ardeshir-Larijani,MM Nasiri Fatmehsari - 《Quantum Machine Intelligence》 被...
Case I: Encoding Specfi c Knowledge for Feature Learning 仍然考虑前面 sentiment classification 的例子,我们希望使用很多标注好的电子产品评论数据来辅助训练电子游戏评论分类的任务。通常分类模型如下: 其中x 表示词库中每个词在该句子中出现的频率,w 表示每个词的情感评价,是基于数据回归得到的。
Modern machine learning models, especially deep neural networks, can often benefit quite significantly from transfer learning. In computer vision, deep convolutional neural networks trained on a large image classification datasets such as ImageNet have proved to be useful for initializing...
Transfer learning for text classification. Adv. Neural Inf. Process. Syst. 2006, 299–306. [Google Scholar] Devlin, J.; Chang, M.W.; Lee, K.; Toutanova, K. BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv 2018, arXiv:1810.04805. [Google Scholar] ...
Text Classification Model is a sequence classification model based on BERT-based encoders. It can be used for a variety of tasks like text classification, sentiment analysis, domain/intent detection for dialogue systems, etc. The model takes a text input and classifies it into predefined categories...