Incremental condition estimation
SIAM Journal on Matrix Analysis and Applications
On computing condition numbers for the nonsymmetric eigenproblem
ACM Transactions on Mathematical Software (TOMS)
The nature of statistical learning theory
The nature of statistical learning theory
ACM Transactions on Mathematical Software (TOMS)
Making large-scale support vector machine learning practical
Advances in kernel methods
Support vector machines: hype or hallelujah?
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Accurate on-line support vector regression
Neural Computation
A novel method for MicroRNA secondary structure prediction using a bottom-up algorithm
Proceedings of the 47th Annual Southeast Regional Conference
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Condition number of a matrix is an important measure in numerical analysis and linear algebra. It is a measure of stability or sensitivity of a matrix to numerical operations. However, the direct computation of the condition number of a matrix is very expensive in terms of CPU and memory cost, and becomes prohibitive for large size matrices. We propose to use data mining techniques to estimate the condition number of a given sparse matrix. In particular, we will use Support Vector Machine (SVM) to predict the condition numbers. That is, after computing the sparsity pattern features of a matrix, we use support vector regression (SVR) to predict its condition number. This Online Condition Number Query System (OCNQS) allows the users to submit their matrices and to obtain predicted condition numbers for their matrices. The accuracy of our prediction methods may not be as precise as the direct computation methods, but it is much faster. Our online system accepts matrices in Harwell-Boeing (HB) format and in standard MATLAB format. The users can use our system to estimate the condition number of their matrices through LAPACK software as well.