We revisit the multiclass support vector machine (SVM) and generalize the formulation to convex l... more We revisit the multiclass support vector machine (SVM) and generalize the formulation to convex loss functions and joint feature maps. Motivated by recent work (Chapelle, 2006) we use logistic loss and softmax to enable gradient based primal optimization. Kernels are incorporated via kernel principal component analysis (KPCA), which naturally leads to approximation methods for large scale problems. We investigate similarities
We have developed an R Interface for our Machine Learning Toolbox SHOGUN. It features algo- rithm... more We have developed an R Interface for our Machine Learning Toolbox SHOGUN. It features algo- rithms to train hidden markov models and learn regression and 2-class classification problems. While the toolbox's focus is on kernel methods such as Support Vector Machines, it also im- plements a number of linear methods like Linear Discriminant Analysis, Linear Programming Machines and Perceptrons. It
North American Chapter of the Association for Computational Linguistics, 2010
The goal of this work is to integrate query similarity metrics as features into a dense model tha... more The goal of this work is to integrate query similarity metrics as features into a dense model that can be trained on large amounts of query log data, in order to rank query rewrites. We propose features that incorpo- rate various notions of syntactic and semantic similarity in a generalized edit distance frame- work. We use the implicit feedback of
We revisit the multiclass support vector machine (SVM) and generalize the formulation to convex l... more We revisit the multiclass support vector machine (SVM) and generalize the formulation to convex loss functions and joint feature maps. Motivated by recent work (Chapelle, 2006) we use logistic loss and softmax to enable gradient based primal optimization. Kernels are incorporated via kernel principal component analysis (KPCA), which naturally leads to approximation methods for large scale problems. We investigate similarities
We have developed an R Interface for our Machine Learning Toolbox SHOGUN. It features algo- rithm... more We have developed an R Interface for our Machine Learning Toolbox SHOGUN. It features algo- rithms to train hidden markov models and learn regression and 2-class classification problems. While the toolbox's focus is on kernel methods such as Support Vector Machines, it also im- plements a number of linear methods like Linear Discriminant Analysis, Linear Programming Machines and Perceptrons. It
North American Chapter of the Association for Computational Linguistics, 2010
The goal of this work is to integrate query similarity metrics as features into a dense model tha... more The goal of this work is to integrate query similarity metrics as features into a dense model that can be trained on large amounts of query log data, in order to rank query rewrites. We propose features that incorpo- rate various notions of syntactic and semantic similarity in a generalized edit distance frame- work. We use the implicit feedback of
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