Fuzzy support vector machines

By Chun-fu Lin, Sheng-de Wang. 2002

1,515 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View (2019, methodology): “In this way, a SVM-based model builds a hyperplane or set of hyperplanes in a high-dimensional space, which can be used for classification, regression, or other tasks like outliers detection [15], [22], [23].”
Machine learning and AI in marketing – Connecting computing power to human insights (2020, neutral): “They argued that while multinomial logit models can help draw insights, in large-scale automatic settings SVM is a better ﬁ t. Evgeniou, Boussios, & Zacharia (2005) developed robust conjoint estimation methods that are almost equivalent to SVM. Similar to Cui & Curry (2005), they also showed that th”
Support Vector Machines in R (2006, background): “One useful extension would allowing to weight the observations (Lin and Wang 1999) which is currently not supported by any of the implementations.”
Robust support vector machines based on the rescaled hinge loss function (2017, background): “In Algorithm 1, v0 can be initialized by either setting it to 1 − or using some existing weight functions [9,10].”
A Survey of Predictive Modeling on Imbalanced Domains (2016, methodology): “This algorithm is based on an SVM variant for handling the problem of outliers and noise called Fuzzy SVM (FSVM) [Lin and Wang 2002] and improves it for also dealing with imbalanced datasets.”
FSVM-CIL: Fuzzy Support Vector Machines for Class Imbalance Learning (2010, methodology): “Fuzzy support vector machines (FSVMs) is a variant of the SVM learning algorithm, which was originally proposed in [17] in order to handle the problem of outliers and noise.”
Uncertainty in big data analytics: survey, opportunities, and challenges (2019, background): “Machine learning Incomplete training samples Inconsistent classification Learning from low veracity and noisy data Active learning [65, 66], Deep learning [15, 63], Fuzzy sets [67], Feature selection [9, 60, 61]”
Data Mining for the Internet of Things: Literature Review and Challenges (2015, methodology): “GSVM (granular support vector machines) [42-44], FSVM (fuzzy support vector machines) [45-47], TWSVMs ((twin support vector machines) [48-50], VaR-SVM (Value-at-risk support vector machines) [51], RSVM (ranking support vector machines) [52].”
A new fuzzy support vector machine to evaluate credit risk (2005, methodology): “Because in the new fuzzy SVM one instance contributes two errors to the total error term in the objective function, while in previous proposed SVM [11] and [ 15 ] one instance contribute just one error, we call the fuzzy SVM in [11] and [15] as unilateral-weighted fuzzy SVM (U-FSVM), our new fuzzy S”
Learning using privileged information: SV M+ and weighted SVM (2013, methodology): “Perhaps the most related in terms of the learning algorithm (SVM) and the interpretation of instance weights are the works on fuzzy SVM (Lin & Wang, 2002), where each data point has a fuzzy class membership represented by a weight between 0 and 1, weighted margin SVM (Wu & Srihari, 2004), where…”