Web Page Classification with Pre-Trained Deep Convolutional Neural Networks
| Main Authors: | Daniel López,, Angélica González Arrieta,, Juan M. Corchado |
|---|---|
| Format: | Proceeding Journal |
| Bahasa: | eng |
| Terbitan: |
, 2017
|
| Subjects: | |
| Online Access: |
https://zenodo.org/record/2677587 |
Daftar Isi:
- In this paper, we propose mining the growing amount of information present on the internet in the form of visual content. We address the problem of web page categorization based on the multimedia elements present on it. To achieve this, our framework leverages a pre-trained deep convolutional neural network model, which is used as a feature extractor for later classification. This paper presents experimental results concerning the effectiveness of different classifiers trained with features extracted at various depths of the convolutional neural network.
- This work has been supported by the European Commission H2020 MSCA-RISE-2014: Marie Skłodowska-Curie project DREAM-GO Enabling Demand Response for short and real-time Efficient And Market Based Smart Grid Operation - An intelligent and real-time simulation approach ref 641794.