Apicella, Andrea (2018) Improving classification models with context knowledge and variable activation functions. [Tesi di dottorato]
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| Item Type: | Tesi di dottorato |
|---|---|
| Resource language: | English |
| Title: | Improving classification models with context knowledge and variable activation functions |
| Creators: | Creators Email Apicella, Andrea and.api.univ@gmail.com |
| Date: | 10 December 2018 |
| Number of Pages: | 112 |
| Institution: | Università degli Studi di Napoli Federico II |
| Department: | Matematica e Applicazioni "Renato Caccioppoli" |
| Dottorato: | Scienze matematiche e informatiche |
| Ciclo di dottorato: | 31 |
| Coordinatore del Corso di dottorato: | nome email De Giovanni, Francesco francesco.degiovanni2@unina.it |
| Tutor: | nome email Festa, Paola UNSPECIFIED Isgrò, Francesco UNSPECIFIED |
| Date: | 10 December 2018 |
| Number of Pages: | 112 |
| Keywords: | machine learning, neural networks, activation functions, ontologies |
| Settori scientifico-disciplinari del MIUR: | Area 01 - Scienze matematiche e informatiche > INF/01 - Informatica Area 01 - Scienze matematiche e informatiche > MAT/09 - Ricerca operativa |
| Date Deposited: | 19 Dec 2018 09:17 |
| Last Modified: | 23 Jun 2020 09:46 |
| URI: | http://www.fedoa.unina.it/id/eprint/12667 |
Collection description
This work proposes two methods to boost the performances of a given classifier: the first one, which works on a Neural Network classifier, is a new type of trainable activation function, that is a function which is adjusted during the learning phase, allowing the network to exploit the data better respect to use a classic activation function with fixed-shape; the second one provides two frameworks to use an external knowledge base to improve the classification results.
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