Apicella, Andrea
(2018)
Improving classification models with context knowledge and variable activation functions.
[Tesi di dottorato]
| 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 |
[error in script]
[error in script]
| 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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