fuzzy rule-based classification systems (frbcs) are highly investigated by researchers due to their noise-stability and  interpretability. unfortunately, generating a rule-base which is sufficiently both accurate and interpretable, is a hard process. rule weighting is one of the approaches to improve the accuracy of a pre-generated rule-base without modifying the original rules. most of the pro...

Typical bounds on generalization of Support Vector Machines are based on the minimum distance between training examples and the separating hyperplane. There has been some debate as to whether a more robust function of the margin distribution could provide generalization bounds. Freund and Schapire (1998) have shown how a diierent function of the margin distribution can be used to bound the numb...

Finding linear classifiers that maximize AUC scores is important in ranking research. This is naturally formulated as a 1-norm hard/soft margin optimization problem over pn pairs of p positive and n negative instances. However, directly solving the optimization problems is impractical since the problem size (pn) is quadratically larger than the given sample size (p + n). In this paper, we give ...

In order to deal with known limitations of the hard margin support vector machine (SVM) for binary classification — such as overfitting and the fact that some data sets are not linearly separable —, a soft margin approach has been proposed in literature [2, 4, 5]. The soft margin SVM allows training data to be misclassified to a certain extent, by introducing slack variables and penalizing the ...

In CDMA (Code DivisionMultiple Access) cellular networks, the soft handoff technique allows a mobile host to communicate with multiple base stations simultaneously, improving the transmission quality of the wireless channel and avoiding disconnection upon to base station switching. In this thesis, we define the soft handoff margin as the area in which the mobile host can use soft handoff and ev...

Boosting methods maximize a hard classiication margin and are known as powerful techniques that do not exhibit overrtting for low noise cases. Also for noisy data boosting will try to enforce a hard margin and thereby give too much weight to outliers, which then leads to the dilemma of non-smooth ts and overrtting. Therefore we propose three algorithms to allow for soft margin classiication by ...

We propose a new discriminative learning framework, called soft margin feature extraction (SMFE), for jointly optimizing the parameters of transformation matrix for feature extraction and of hidden Markov models (HMMs) for acoustic modeling. SMFE extends our previous work of soft margin estimation (SME) to feature extraction. Tested on the TIDIGITS connected digit recognition task, the proposed...

Margin-based classifiers have been popular in both machine learning and statistics for classification problems. Among numerous classifiers, some are hard classifiers while some are soft ones. Soft classifiers explicitly estimate the class conditional probabilities and then perform classification based on estimated probabilities. In contrast, hard classifiers directly target on the classificatio...