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A3M: The second paper of A3M Project has been accepted in the Journal of Philosophy !
Reviewed by ngohoanhkhoi
on
September 30, 2019
Rating: 5
Reviewed by ngohoanhkhoi
on
September 30, 2019
Rating: 5
A3M: The first paper of A3M Project has been accepted in the International Conference of Vietnam !
Our paper of A3M Project "Distinct gemological properties of Sa Huynh glass ornaments" has been accepted in the Workshop On Heritage Values Of Ly Son - Sa Huynh Geopark, which will be celebrated in Quang Ngai, Vietnam in 2019.
A3M: The first paper of A3M Project has been accepted in the International Conference of Vietnam !
Reviewed by ngohoanhkhoi
on
February 01, 2019
Rating: 5
Reviewed by ngohoanhkhoi
on
February 01, 2019
Rating: 5
mOC-iSVM will be adapted for A3M System
The mOC-iSVM will be adapted for the Antique Artifacts Automatic Identification Machine for Vietnamese Ancient Glass Jewelry Project (so called A3M-VAG). This project is a collaboration between Nam Can Tho University (NCTU), UNESCO Centre for Research and Preservation for the Vietnamese Antiquity (UNESCO-VNA), Hanoi University of Mining and Geology (HUMG), Quang Ngai Museum (QNM), Institute of Archeology (IA). The director of the project is Dr. Ngo Ho Anh Khoi.
mOC-iSVM will be adapted for A3M System
Reviewed by ngohoanhkhoi
on
January 01, 2018
Rating: 5
Reviewed by ngohoanhkhoi
on
January 01, 2018
Rating: 5
Introduction
One of the most difficult problems with classification is that the concepts and their relations are highly dependent on some hidden context. As consequences, any information about number of concept, description of concepts, relation between the concepts should ideally be totally well-known. Most often, a priori or arbitrary knowledge is injected in the learning scheme to simplify the task and make it more accurate. This is the reason why many supervised classification tasks were done under the assumption that the contexts are fixed and known before the learning. Recently, a great attention has been paid on classification in data streams where the concepts can change over the time. The classification of data delivered in stream mainly use very specific techniques and adaptations. Indeed, for many real processes, the categorization of the data is not so simple, even in a supervised mode. Each time the user encounters a new data, he can decide to change the previously established classification, for example by merging classes, creating new ones and so on. More generally, the concepts can evolve over the time depending on encountered data : it can lead to concepts fusion or splitting, to concepts creation, extension or deletion. The knowledge and expertise are not precisely accessible at the beginning of the process mainly because final needs and usages can vary.
This multi One-Class incremental SVM (so called mOC-iSVM) system has been proposed as a solution to learning problems in a context of data streams. In the field of incremental learning, different challenges are addressed to adapt the system to stationary (fixed concepts) or non-stationary environments (concept drift), also challenged with adding, removing, merging, splitting classes. The mOC-iSVM has shown its ability to improve the classification accuracy over the time, with incoming new labeled data, without performing any complete retraining.
This multi One-Class incremental SVM (so called mOC-iSVM) system has been proposed as a solution to learning problems in a context of data streams. In the field of incremental learning, different challenges are addressed to adapt the system to stationary (fixed concepts) or non-stationary environments (concept drift), also challenged with adding, removing, merging, splitting classes. The mOC-iSVM has shown its ability to improve the classification accuracy over the time, with incoming new labeled data, without performing any complete retraining.
Introduction
![Introduction]()
Reviewed by ngohoanhkhoi
on
October 30, 2016
Rating: 5
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