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- Ratthachat Chatpatanasiri, Teesid Korsrilabutr, Pasakorn Tangchanachaianan, Boonserm Kijsirikul
- Neurocomputing
- 2010

This paper focuses on developing a new framework of kernelizing Mahalanobis distance learners. The new KPCA trick framework offers several practical advantages over the classical kernel trick framework, e.g. no mathematical formulas and no reprogramming are required for a kernel implementation, a way to speed up an algorithm is provided with no extra work,… (More)

This paper presents a method of extending Support Vector Machines (SVMs) for dealing with multiclass problems. Motivated by the Decision Directed Acyclic Graph (DDAG), we propose the Adaptive DAG (ADAG): a modified structure of the DDAG that has a lower number of decision levels and reduces the dependency on the sequence of nodes. Thus, the ADAG improves… (More)

- Boonserm Kijsirikul, Nitiwut Ussivakul, Surapant Meknavin
- PRICAI
- 2002

This paper presents a method, called Adaptive Directed Acyclic Graph (ADAG), to extend Support Vector Machines (SVMs) for multiclass classification. The ADAG is based on the previous approach, the Decision Directed Acyclic Graph (DDAG), and is designed to remedy some weakness of the DDAG caused by its structure. We prove that the expected accuracy of the… (More)

- Tanasanee Phienthrakul, Boonserm Kijsirikul
- GECCO
- 2005

In support vector machines (SVM), the kernel functions which compute dot product in feature space significantly affect the performance of classifiers. Each kernel function is suitable for some tasks. A universal kernel is not possible, and the kernel must be chosen for the tasks under consideration by hand. In order to obtain a flexible kernel function, a… (More)

- Thimaporn Phetkaew, Wanchai Rivepiboon, Boonserm Kijsirikul
- JACIII
- 2003

- Sukree Sinthupinyo, Cholwich Nattee, Masayuki Numao, Takashi Okada, Boonserm Kijsirikul
- JSAI Workshops
- 2004

In this paper, we propose an approach which can improve Inductive Logic Programming in multiclass problems. This approach is based on the idea that if a whole rule cannot be applied to an example, some partial matches of the rule can be useful. The most suitable class should be the class whose important partial matches cover the example more than those from… (More)

- Nuttakorn Thubthong, Boonserm Kijsirikul, Apirath Pusittrakul
- Computational Intelligence
- 2002

- Nuttakorn Thubthong, Boonserm Kijsirikul
- International Journal of Uncertainty, Fuzziness…
- 2001

The Support Vector Machine (SVM) has recently been introduced as a new pattern classification technique. It learns the boundary regions between samples belonging to two classes by mapping the input samples into a high dimensional space, and seeking a separating hyperplane in this space. This paper describes an application of SVMs to two phoneme recognition… (More)