An Introduction to Support Vector Machines and Other Kernel‐based Learning Methods

By Alex M. Andrew. 2001

1,630 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0b41df73cf0aeda76b060a735d9fcc252ee761c9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

Citations in the corpus

Ontology Matching (2007, methodology): “Support vector machines (SVM) are supervised learning methods used for classification, regression and other tasks (Cristianini and Shawe-Taylor 2000).”
Inductive Conformal Prediction: Theory and Application to Neural Networks (2008, methodology): “A demonstration of the crudeness of PAC bounds can be found in (Nouretdinov et al., 2001a), where there is an example of Littlestone and Warmuth’s bound (found in (Cristianini & Shawe-Taylor, 2000), Theorems 4.25 and 6.8) applied to the USPS data set.”
Automatic classification of patients with Alzheimer's disease from structural MRI: A comparison of ten methods using the ADNI database (2011, methodology): “A convenient approach to combine different SVM-based methods is to consider that the resulting classifier is a SVM which kernel is a linear Table 4 Classification results CN vs AD.”
Inductive Confidence Machines for Regression (2002, methodology): “Medians, upper and lower quartiles, and 10th and 90th percentile of the distributions of the predictive interval widths for the method using (1) as strangeness value”
Surface EMG in advanced hand prosthetics (2008, background): “Support Vector Machines (SVMs; see, e.g. Boser et al. 1992; Burges 1998; Cristianini and Shawe-Taylor 2000) are machine learning methods able to determine the best candidate”
Prediction of CTL epitopes using QM, SVM and ANN techniques. (2004, methodology): “The SVM implementation is achieved by using the package SVMLIGHT [29,30].”
Finite-Time Bounds for Fitted Value Iteration (2008, methodology): “Another possibility is to use the kernel mapping idea underlying many recent state-of-the-art supervised-learning methods, such as support-vector machines, support-vector regression or Gaussian processes (Cristianini and Shawe-Taylor, 2000).”
Spectral–Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques (2009, neutral): “Although the pixel wise SVM classifier gives good classification accuracies, it is a computationally demanding algorithm for high-dimensional data and/or when the number of training samples is large [19], [68].”
Incremental Support Vector Machine Learning: A Local Approach (2001, methodology): “based on a criterion derived from a bound an error generalization from [7] and allows to determine a relevant neighbourhood size at each learning step.”