Predicting structured objects with support vector machines

Predicting Bankruptcy with Support Vector Machines

Predicting Time Series with Support Vector Machines

Support Vector Machines are used for time series prediction and compared to radial basis function networks. We make use of two diierent cost functions for Support Vectors: training with (i) an insensitive loss and (ii) Huber's robust loss function and discuss how t o c hoose the regularization parameters in these models. Two applications are considered: data from (a) a noisy (normal and uniform...

Predicting Time Series with Support Vector Machines

Support Vector Machines are used for time series prediction and compared to radial basis function networks. We make use of two diierent cost functions for Support Vectors: training with (i) an insensitive loss and (ii) Huber's robust loss function and discuss how to choose the regularization parameters in these models. Two applications are considered: data from (a) a noisy (normal and uniform n...

Structured variable selection in support vector machines

When applying the support vector machine (SVM) to highdimensional classification problems, we often impose a sparse structure in the SVM to eliminate the influences of the irrelevant predictors. The lasso and other variable selection techniques have been successfully used in the SVM to perform automatic variable selection. In some problems, there is a natural hierarchical structure among the va...

Structured Support Vector Machines for Speech Recognition

Discriminative training criteria and discriminative models are two ešective improvements for HMM-based speech recognition. is thesis proposed a structured support vector machine (SSVM) framework suitable for medium to large vocabulary continuous speech recognition. An important aspect of structured SVMs is the form of features. Several previously proposed features in the eld are summarized in ...