1 Summary Microsoft Cambridge at TREC 2002: Filtering track (2008)
S E Robertson, S Walker, H Zaragoza, R Herbrich
Six runs were submitted for the Adaptive Filtering track, four on the adaptive filtering task (ok11af??), and two on the routing task (msPUM?). The adaptive filtering system has been somewhat...
1 Summary Microsoft Cambridge at TREC 2002: Filtering track (2008)
S E Robertson, S Walker, H Zaragoza, R Herbrich
Six runs were submitted for the Adaptive Filtering track, four on the adaptive filtering task (ok11af??), and two on the routing task (msPUM?). The adaptive filtering system has been somewhat...
Kernel Constrained Covariance for Dependence Measurement (2005)
Gretton, A., Smola, A.J., Bousquet, O., Herbrich, R., Belitski, A., Augath, M., ...
We discuss reproducing kernel Hilbert space (RKHS)-based measures of statistical dependence, with emphasis on constrained covariance (COCO), a novel criterion to test dependence of random variables....
Kernel Methods for Measuring Independence (2005)
Gretton, A., Herbrich, R., Smola, A., Bousquet, O., Schölkopf, B.
We introduce two new functionals, the constrained covariance and the kernel mutual information, to measure the degree of independence of random variables. These quantities are both based on the...
Online Bayes Point Machines (2003)
Harrington, E., Herbrich, R., Kivinen, J., Platt, J., Williamson, R.C.
We present a new and simple algorithm for learning large margin classifiers that works in a truly online manner. The algorithm generates a linear classifier by avergaing the weights associated with...
Online Bayes Point Machines (2003)
Harrington, E., Herbrich, R., Kivinen, J., Platt, J., Williamson, R.C.
We present a new and simple algorithm for learning large margin classifiers that works in a truly online manner. The algorithm generates a linear classifier by avergaing the weights associated with...
Online Bayes Point Machines (2003)
Harrington, E., Herbrich, R., Kivinen, J., Platt, J., Williamson, R.C.
We present a new and simple algorithm for learning large margin classifiers that works in a truly online manner. The algorithm generates a linear classifier by avergaing the weights associated with...
Online Bayes Point Machines (2003)
Harrington, E., Herbrich, R., Kivinen, J., Platt, J., Williamson, R.C.
We present a new and simple algorithm for learning large margin classifiers that works in a truly online manner. The algorithm generates a linear classifier by avergaing the weights associated with...
Classification on proximity data with LP-machines (1999)
Graepel, T., Herbrich, R., Schölkopf, B., Smola, A.J., Bartlett, P.L., ...
Microsoft Cambridge at TREC 2002: Filtering track
S E Robertson, S Walker, H Zaragoza, R Herbrich
this paper. For these comparisons we consider only the msPUMb model. We note that these results are better than those submitted originally: after submission we discovered an error in our data...
Microsoft Cambridge at TREC 2002: Filtering track
Robertson Walker Zaragoza, S E Robertson, S Walker, H Zaragoza, R Herbrich
this paper. For these comparisons we consider only the msPUMb model. We note that these results are better than those submitted originally: after submission we discovered an error in our data...