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We investigate essential relationships between generalization capabilities and fuzziness of fuzzy classifiers (viz., the classifiers whose outputs are vectors of membership grades of a pattern to the individual classes). The study makes a claim and offers sound evidence behind the observation that higher fuzziness of a fuzzy classifier may imply better generalization aspects of the classifier, especially...
Behavioral biometric on mobile devices has begun to gain attention in recent years and the feasibility of touch gestures as a novel biometric modality has been investigated lately. In this paper, we propose a novel Graphic Touch Gesture Feature (GTGF) to extract the identity traits from the touch traces. The traces' movement and pressure dynamics are represented by intensity values and shapes of the...
Due to hypersensitivity to sound, patients with autism spectrum disorders (ASD) can feel frustrated and even profoundly fearful when talking with multiple speakers. This exacerbates their impairments in social interaction and communication. We propose a fully interactive system that allows ASD patient to focus on a single auditory stream (a person's voice) according to their preference during conversations...
Decision tree is one of the most popular and widely used classification models in machine learning. The discretization of continuous-valued attributes plays an important role in decision tree generation. In this paper, we improve Fayyad's discretization method which uses the average class entropy of candidate partitions to select boundaries for discretization. Our method can reduce the number of candidate...
This paper improves a method of sample selection based on maximum entropy. Compared with the original method, the improved one takes the probability distribution of unlabeled instances into consideration. It selects the instances which can reduce the uncertainty of the whole unlabeled set to a great extent. The uncertainty reduction of the whole unlabeled set caused by an instance is measured by the...
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