The source codes of KIERA (doi:10.21979/N9/P9DFJH)

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Part 1: Document Description
Part 2: Study Description
Part 5: Other Study-Related Materials
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Document Description

Citation

Title:

The source codes of KIERA

Identification Number:

doi:10.21979/N9/P9DFJH

Distributor:

DR-NTU (Data)

Date of Distribution:

2022-06-24

Version:

1

Bibliographic Citation:

Pratama, Mahardhika; Ashfahani, Andri; Lughofer, Edwin, 2022, "The source codes of KIERA", https://doi.org/10.21979/N9/P9DFJH, DR-NTU (Data), V1

Study Description

Citation

Title:

The source codes of KIERA

Identification Number:

doi:10.21979/N9/P9DFJH

Authoring Entity:

Pratama, Mahardhika (Nanyang Technological University)

Ashfahani, Andri (Nanyang Technological University)

Lughofer, Edwin (DKBMS, Johanes Kepler University, Linz, Austria)

Software used in Production:

Python

Grant Number:

start-up grant

Distributor:

DR-NTU (Data)

Access Authority:

Pratama, Mahardhika

Depositor:

Pratama, Mahardhika

Date of Deposit:

2022-05-12

Holdings Information:

https://doi.org/10.21979/N9/P9DFJH

Study Scope

Keywords:

Computer and Information Science, Computer and Information Science, <placeholder>

Abstract:

the source code of KIERA " Unsupervised Continual Learning via Self-Adaptive Deep Clustering Approach "

Kind of Data:

code

Methodology and Processing

Sources Statement

Data Access

Notes:

Please refer to the LICENSE.md file in this dataset.

Other Study Description Materials

Related Publications

Citation

Identification Number:

10.1007/978-3-031-17587-9_4

Bibliographic Citation:

Pratama, M., Ashfahani, A., & Lughofer, E. (2022, September). Unsupervised continual learning via self-adaptive deep clustering approach. In Continual Semi-Supervised Learning: First International Workshop, CSSL 2021, Virtual Event, August 19–20, 2021, Revised Selected Papers (pp. 48-61). Cham: Springer International Publishing.

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