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Semi-supervised classification with stacked autoencoder

An autoencoder and encoder technology, applied in the field of artificial intelligence, can solve problems such as the inability to learn distinguishing features

Pending Publication Date: 2019-04-26
KONINKLJIJKE PHILIPS NV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Therefore, they cannot jointly learn discriminative features from both unlabeled and labeled data

Method used

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  • Semi-supervised classification with stacked autoencoder
  • Semi-supervised classification with stacked autoencoder
  • Semi-supervised classification with stacked autoencoder

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Embodiment Construction

[0017] Classification methods based on deep learning usually rely on a large amount of labeled data. However, the high cost of collecting labeled data has limited researchers from applying these techniques to many natural language processing tasks. Current semi-supervised methods for deep learning mainly use unlabeled data to learn word embeddings, which are then used for supervised classification, but these learned vectors do not directly benefit from supervision. Semi-supervised learning aims to improve the performance of supervised methods by exploiting both unlabeled and labeled data. There have been some limited attempts to use deep learning for semi-supervised sentence classification, e.g., using convolutional neural networks (“CNN”) and / or long short-term memory networks (“LSTM”) to learn word embeddings from unlabeled training data , and then use these embeddings for supervised classification. While these efforts can mitigate some of the errors in the sentence classi...

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Abstract

Techniques described herein relate to semi-supervised training and application of stacked autoencoders and other classifiers for predictive and other purposes. In various embodiments, a semi-supervised model (108) may be trained for sentence classification, and may combine what is referred to herein as a "residual stacked de-noising autoencoder" ("RSDA") (220), which may be unsupervised, with a supervised classifier (218) such as a classification neural network (e.g., a multilayer perceptron, or "MLP"). In various embodiments, the RSDA may be a stacked denoising autoencoder that may or may notinclude one or more residual connections. If present, the residual connections may help the RSDA "remember" forgotten information across multiple layers. In various embodiments, the semi-supervised model may be trained with unlabeled data (for the RSDA) and labeled data (for the classifier) simultaneously.

Description

technical field [0001] Various embodiments described herein relate generally to artificial intelligence. More particularly, but not exclusively, various methods and apparatus disclosed herein relate to the semi-supervised training and application of stacked autoencoders and other classifiers for predictive and other purposes. Background technique [0002] Classification methods based on deep learning usually rely on a large amount of labeled data. However, the high cost of collecting labeled data has limited researchers from applying these techniques to many natural language processing tasks. Current semi-supervised methods for deep learning mainly use unlabeled data to learn word embeddings, which are then used for supervised classification, but these learned vectors do not directly benefit from supervision. Semi-supervised learning aims to improve the performance of supervised methods by exploiting both unlabeled and labeled data. There have been some limited attempts t...

Claims

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Application Information

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IPC IPC(8): G06N3/04G06N3/08
CPCG06N3/084G06N3/045G06F18/24G16H50/20G06N3/088
Inventor R·加艾尼S·S·阿尔哈桑O·F·法里K·李V·达特拉A·卡迪尔柳俊毅A·普拉卡什
Owner KONINKLJIJKE PHILIPS NV