Results 21 to 30 of about 74,247 (264)

Wavelet Support Vector Machine [PDF]

IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 2004
An admissible support vector (SV) kernel (the wavelet kernel), by which we can construct a wavelet support vector machine (SVM), is presented. The wavelet kernel is a kind of multidimensional wavelet function that can approximate arbitrary nonlinear functions. The existence of wavelet kernels is proven by results of theoretic analysis.
Li Zhang 0004, Weida Zhou, Licheng Jiao
openaire   +2 more sources

Oblique Support Vector Machines

Informatica, 2005
In this paper we propose a modified framework of support vector machines, called Oblique Support Vector Machines(OSVMs), to improve the capability of classification. The principle of OSVMs is joining an orthogonal vector into weight vector in order to rotate the support hyperplanes.
Chih-Chia Yao, Pao-Ta Yu
openaire   +3 more sources

Sparse Deconvolution Using Support Vector Machines

EURASIP Journal on Advances in Signal Processing, 2008
Sparse deconvolution is a classical subject in digital signal processing, having many practical applications. Support vector machine (SVM) algorithms show a series of characteristics, such as sparse solutions and implicit regularization, which make them ...
Aníbal R. Figueiras-Vidal, Carlos M. Cruz, Gustavo Camps-Valls, Jordi Muñoz-Marí, Manel Martínez-Ramón, José Luis Rojo-Álvarez   +5 more
doaj   +1 more source

Breakdown Point of Robust Support Vector Machines

Entropy, 2017
Support vector machine (SVM) is one of the most successful learning methods for solving classification problems. Despite its popularity, SVM has the serious drawback that it is sensitive to outliers in training samples.
Takafumi Kanamori, Shuhei Fujiwara, Akiko Takeda   +2 more
doaj   +1 more source

Extensions of the SVM Method to the Non-Linearly Separable Data [PDF]

Informatică economică, 2013
The main aim of the paper is to briefly investigate the most significant topics of the currently used methodologies of solving and implementing SVM-based classifier.
Luminita STATE, Catalina COCIANU, Cristian USCATU, Marinela MIRCEA   +3 more
doaj   +1 more source

Boosting Support Vector Machines [PDF]

Revista de Ingeniería, 2006
En este articulo, se presenta un algoritmo de clasificacion binaria basado en Support Vector Machines (Maquinas de Vectores de Soporte) que combinado apropiadamente con tecnicas de Boosting consigue un mejor desempeno en cuanto a tiempo de entrenamiento y conserva caracteristicas similares de generalizacion con un modelo de igual complejidad pero de ...
Elkin García, Fernando Lozano
openaire   +1 more source

Robust relative margin support vector machines

Journal of Algorithms & Computational Technology, 2017
Recently, a class of classifiers, called relative margin machine, has been developed. Relative margin machine has shown significant improvements over the large margin counterparts on real-world problems.
Yunyan Song, Wenxin Zhu, Yingyuan Xiao, Ping Zhong   +3 more
doaj   +1 more source

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, Geoffrey J. Gordon   +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, Yingcong Li, Christos Thrampoulidis, Samet Oymak   +3 more
openaire   +2 more sources

The complexity of quantum support vector machines [PDF]

Quantum
Quantum support vector machines employ quantum circuits to define the kernel function. It has been shown that this approach offers a provable exponential speedup compared to any known classical algorithm for certain data sets. The training of such models
Gian Gentinetta, Arne Thomsen, David Sutter, Stefan Woerner   +3 more
doaj   +1 more source