Results 31 to 40 of about 108,016 (310)
The support vector decomposition machine [PDF]
Proceedings of the 23rd international conference on Machine learning - ICML '06, 2006 In machine learning problems with tens of thousands of features and only dozens or hundreds of independent training examples, dimensionality reduction is essential for good learning performance. In previous work, many researchers have treated the learning problem in two separate phases: first use an algorithm such as singular value decomposition to ...
Francisco Pereira 0001 +1 more
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Transformers as Support Vector Machines
CoRR, 2023 Since its inception in "Attention Is All You Need", transformer architecture has led to revolutionary advancements in NLP. The attention layer within the transformer admits a sequence of input tokens $X$ and makes them interact through pairwise similarities computed as softmax$(XQK^\top X^\top)$, where $(K,Q)$ are the trainable key-query parameters. In
Davoud Ataee Tarzanagh +3 more
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Cascade Support Vector Machines with Dimensionality Reduction
Applied Computational Intelligence and Soft Computing, 2015 Cascade support vector machines have been introduced as extension of classic support vector machines that allow a fast training on large data sets. In this work, we combine cascade support vector machines with dimensionality reduction based preprocessing.
Oliver Kramer
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Twin Bounded Weighted Relaxed Support Vector Machines
IEEE Access, 2019 Data distribution has an important role in classification. The problem of imbalanced data has occurred when the distribution of one class, which usually attends more interest, is negligible compared with other class.
Fatemeh Alamdar +2 more
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Applied Sciences, 2020 Support vector machines (SVMs) are a well-known classifier due to their superior classification performance. They are defined by a hyperplane, which separates two classes with the largest margin.
Minho Ryu, Kichun Lee
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Explaining Support Vector Machines: A Color Based Nomogram. [PDF]
PLoS ONE, 2016 PROBLEM SETTING:Support vector machines (SVMs) are very popular tools for classification, regression and other problems. Due to the large choice of kernels they can be applied with, a large variety of data can be analysed using these tools.
Vanya Van Belle +4 more
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Massive Data Classification via Unconstrained Support Vector Machines
, 2006 A highly accurate algorithm, based on support vector machines formulated as linear programs [13, 1], is proposed here as a completely unconstrained minimization problem [15].
O. L. Mangasarian +3 more
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Recent advances on support vector machines research
Technological and Economic Development of Economy, 2012 Support vector machines (SVMs), with their roots in Statistical Learning Theory (SLT) and optimization methods, have become powerful tools for problem solution in machine learning.
Yingjie Tian, Yong Shi, Xiaohui Liu
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Damage Diagnosis of Bolt Loosening via Vector Autoregressive - Support Vector Machines
Hittite Journal of Science and Engineering, 2020 Developments in engineering techniques have concentrated on how to build better solutions for engineering structures in order to main the integrity and to reduce the costs in operations.
Mahmut Pekedis
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Bird Species Recognition Using Support Vector Machines
EURASIP Journal on Advances in Signal Processing, 2007 Automatic identification of bird species by their vocalization is studied in this paper. Bird sounds are represented with two different parametric representations: (i) the mel-cepstrum parameters and (ii) a set of low-level signal parameters, both of ...
Seppo Fagerlund
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