Predicting Structured Data (2007)
BakIr, G.H., Hofmann, T., Schölkopf, B., Smola, A.J., Taskar, B., Vishwanathan, S.V.N.
Machine learning develops intelligent computer systems that are able to generalize from previously seen examples. A new domain of machine learning, in which the prediction must satisfy the additional...
On the Pre-Image Problem in Kernel Methods (2007)
BakIr, G.H., Schölkopf, B., Weston, J.
In this chapter we are concerned with the problem of reconstructing patterns from their representation in feature space, known as the pre-image problem. We review existing algorithms and propose a...
Predicting Structured Data (2007)
BakIr, G.H., Hofmann, T., Schölkopf, B., Smola, A.J., Taskar, B., Vishwanathan, S.V.N.
Machine learning develops intelligent computer systems that are able to generalize from previously seen examples. A new domain of machine learning, in which the prediction must satisfy the additional...
Breaking SVM Complexity with Cross-Training (2005)
Bakir, G.H., Bottou, L., Weston, J., Saul, L.K., Weiss, Y., Bottou, L.
We propose an algorithm for selectively removing examples from the training set using probabilistic estimates related to editing algorithms (Devijver and Kittler82). The procedure creates a separable...
Learning to Find Graph Pre-Images (2004)
The recent development of graph kernel functions has made it possible to apply well-established machine learning methods to graphs. However, to allow for analyses that yield a graph as a result, it...
Learning Depth From Stereo (2004)
Sinz,F., Quiñonero-Candela,J., Bakir,G.H., Rasmussen,C.E., Franz,M.O.
We compare two approaches to the problem of estimating the depth of a point in space from observing its image position in two different cameras: 1.~The classical photogrammetric approach explicitly...
Efficient Approximations for Support Vector Machines in Object Detection (2004)
Kienzle,W., Bakir,G.H., Franz,M.O., Schölkopf,B.
We present a new approximation scheme for support vector decision functions in object detection. In the present approach we are building on an existing algorithm where the set of support vectors is...
Learning to Find Pre-Images (2004)
Bakir,G.H., Weston,J., Schölkopf,B.
We consider the problem of reconstructing patterns from a feature map. Learning algorithms using kernels to operate in a reproducing kernel Hilbert space (RKHS) express their solutions in terms of...
Multivariate Regression via Stiefel Manifold Constraints (2004)
Bakir,G.H., Gretton,A., Franz,M.O., Schölkopf,B.
We introduce a learning technique for regression between high-dimensional spaces. Standard methods typically reduce this task to many one-dimensional problems, with each output dimension considered...
Learning Depth From Stereo (2004)
Sinz, F., Quiñonero-Candela, J., Bakir, G.H., Rasmussen, C.E., Franz, M.O., Rasmussen, C. E., ...
We compare two approaches to the problem of estimating the depth of a point in space from observing its image position in two different cameras: 1.~The classical photogrammetric approach explicitly...
Efficient Approximations for Support Vector Machines in Object Detection (2004)
Kienzle, W., Bakir, G.H., Franz, M.O., Schölkopf, B., Rasmussen, C. E., Bülthoff, H. H., ...
We present a new approximation scheme for support vector decision functions in object detection. In the present approach we are building on an existing algorithm where the set of support vectors is...
Learning to Find Pre-Images (2004)
Bakir, G.H., Weston, J., Schölkopf, B., Thrun, S., Saul, L., Schölkopf, B.
We consider the problem of reconstructing patterns from a feature map. Learning algorithms using kernels to operate in a reproducing kernel Hilbert space (RKHS) express their solutions in terms of...
Learning to Find Graph Pre-Images (2004)
Bakir, G.H., Zien, A., Tsuda, K., Rasmussen, C. E., Buelthoff, H. H., Giese, M. A., ...
The recent development of graph kernel functions has made it possible to apply well-established machine learning methods to graphs. However, to allow for analyses that yield a graph as a result, it...
Multivariate Regression via Stiefel Manifold Constraints (2004)
Bakir, G.H., Gretton, A., Franz, M.O., Schölkopf, B., Rasmussen, C. E., Bülthoff, H. H., ...
We introduce a learning technique for regression between high-dimensional spaces. Standard methods typically reduce this task to many one-dimensional problems, with each output dimension considered...
On the Representation, Learning and Transfer of Spatio-Temporal Movement Characteristics (2003)
Ilg,W., Bakir,G.H., Mezger,J., Giese,M.A.
In this paper we present a learning-based approach for the modelling of complex movement sequences. Based on the method of Spatio-Temporal Morphable Models (STMMS. We derive a hierarchical algorithm...
On the Representation, Learning and Transfer of Spatio-Temporal Movement Characteristics (2003)
Ilg, W., Bakir, G.H., Mezger, J., Giese, M.A.
In this paper we present a learning-based approach for the modelling of complex movement sequences. Based on the method of Spatio-Temporal Morphable Models (STMMS. We derive a hierarchical algorithm...