Estimating the Generalization Performance of an SVM Efficiently

By Thorsten Joachims. International Conference on Machine Learning, 2000

441 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a8c25c5a35a4bcba6889efe5de088ea914bff0e8

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In Artificial Intelligence

Citations in the corpus

Learning and evaluating classifiers under sample selection bias (2004, methodology): “In its basic form, the support vector machine (SVM) algorithm (Joachims, 2000a) learns the parameters a and b describing a linear decision rule whose sign determines the label of an example, so that the smallest distance between each training example and the decision boundary, i.e. the margin, is…”
Choosing Multiple Parameters for Support Vector Machines (2002, methodology): “A similar derivation as the one used in the span bound has been proposed in [10], where the leave-one-out error is bounded by jfp; 2 pR 2 > ypf (xp)gj, with 0 K(xi;xi) R 2; 8i.”
Incremental Support Vector Machine Learning: A Local Approach (2001, background): “The same holds for Joachims’ e y estimators [10, 11].”
Detecting Concept Drift with Support Vector Machines (2000, methodology): “It is proven in Joachims (2000) that d is an approximate upper bound on the number of leave-one-out errors in the training set.”
Wavelet support vector machine (2004, methodology): “The parameters for wavelet kernel and for the Gaussian kernel are selected by using cross validation that is in wide use [20], [21].”
Distributional Word Clusters vs. Words for Text Categorization (2003, methodology): “Joachims (1999) also experiments with SVM categorization of the WebKB dataset (see details of these results in the last row in Table 1).”
Learning drifting concepts: Example selection vs. example weighting (2004, positive): “We extend the approach of [13] by using some special properties of support vector machines that will prove useful in handling concept drift, for example effective and efﬁcient performance estimation [8] and example weighting [12,15].”
Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel (2003, background): “2 is solved using all the examples, it is possible to give bounds on the LOO error (Joachims, 2000; Vapnik & Chapelle, 2000) without solving the quadratic programs obtained by leaving out one example at a time.”
A hybrid machine learning approach to network anomaly detection (2007, methodology): “The SVM is already known as the best learning algorithm for binary classification [5,6,12,17,20,23,28,37,40,42].”
Support vector machines: hype or hallelujah? (2000, methodology): “As an example, we consider a recent scheme proposed by Joachims [ 30 ].”