Support vector machine (SVM) is a theoretically well motivated algorithm developed from statistical learning theory, that have shown good performance in many fields. In spite of its success, it still suffers from a noise sensitivity problem.To relax this problem, the SVM was extended by the introduction of fuzzy memberships to the fuzzy SVM (FSVM). The FSVM also has been extended further in two ways: by adopting a different objective function with the help of domain-specific knowledge and by employing a different membership calculation method. In this paper, we propose a new membership calculation method, that belongs to the second group. It is different from previous ones in that it does not assume any simple data distribution and does not need any prior knowledge. The proposed method is based on reconstruction error, which measures the agreement between the overall data structure and a data point. Thus the reconstruction error can represent the degree of outlier-ness and help in achieving noise robustness. Experimental results with synthetic and real data sets also support this.
Fuzzy c-means (FCM) is a simple but powerful clustering method using the concept of fuzzy sets that has been proved to be useful in many areas. There are, however, several well known problems with FCM, such as sensitivity to initialization, sensitivity to outliers, and limitation to convex clusters. In this paper, global fuzzy c-means (G-FCM) and kernel fuzzy c-means (K-FCM) are combined and extended to form a non-linear variant of G-FCM, called kernelized global fuzzy c-means (KG-FCM). G-FCM is a variant of FCM that uses an incremental seed selection method and is effective in alleviating sensitivity to initialization. There are several approaches to reduce the influence of noise and properly partition non-convex clusters, and K-FCM is one. K-FCM is used in this paper because it can easily be extended with different kernels, which provide sufficient flexibility to allow for resolution of the shortcomings of FCM. By combining G-FCM and K-FCM, KG-FCM can resolve the shortcomings mentioned above. The usefulness of the proposed method is demonstrated by experiments using artificial and real world data sets.
To cultivate creative talent, ways to learn creative problem-solving skills is needed, and one of them is programming. Arduino is a well-known tool used for programming education and the usefulness has been demonstrated in various case studies. However, there are several problems in existing Arduino-compatible kits as education tools, including the need for understanding hardware and the difficulty of expanding the kits with third-party hardware. In this paper, the design of an Arduino-compatible modular kit, called as FRUTO, is proposed that can be easily connected and conveniently programmed to overcome the problems. The structure and features of the FRUTO kit that implements the proposed design are also shown. The FRUTO kit consists of the FRUTO module that uses a unified connector for easy and intuitive connection and the FRUTO library that abstracts hardware-dependent code for easy programming. The FRUTO kit is easier to use and more scalable than existing kits. Even more, it can be used in various ways depending on the students' familiarity with hardware and programming. These strengths will make the kit to be an appropriate tool for various microcontroller-related education as well as programming education.
Fuzzy c-means (FCM) and possibilistic c-means (PCM) are the two most well-known clustering algorithms in fuzzy clustering area, and have been applied in many areas with their original or modified forms. However, FCM's noise sensitivity problem and PCM's overlapping cluster problem are also well known. Recently there have been several attempts to combine both of them to mitigate these problems and possibilistic fuzzy c-means (PFCM) showed promising results. In this paper, we propose a modified PFCM using regularization to reduce noise sensitivity in PFCM further. Regularization is a wellknown technique to make a solution space smooth and an algorithm noise insensitive. The proposed algorithm, PFCM with regularization (PFCM-R), takes advantage of regularization and further reduce the effect of noise. Experimental results are given and show that PFCM-R is better than existing methods in noisy conditions.
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