The need for open source software in machine learning (2007)
Sonnenburg, S., Braun, M.L., Ong, C.S., Bengio, S., Bottou, L., Holmes, G., ...
Open source tools have recently reached a level of maturity which makes them suitable for building large-scale real-world systems. At the same time, the field of machine learning has developed a...
The Need for Open Source Software in Machine Learning (2007)
Sonnenburg, S., Braun, M.L., Ong, C.S., Bengio, S., Bottou, L., Holmes, G., ...
Open source tools have recently reached a level of maturity which makes them suitable for building large-scale real-world systems. At the same time, the field of machine learning has developed a...
To appear in “Large-Scale Kernel Machines”, (2007)
Yoshua Bengio, Yann Lecun, L. Bottou, O. Chapelle, ...
One long-term goal of machine learning research is to produce methods that are applicable to highly complex tasks, such as perception (vision, audition), reasoning, intelligent control, and other...
To appear in “Large-Scale Kernel Machines”, (2007)
Yoshua Bengio, Yann Lecun, L. Bottou, O. Chapelle, ...
One long-term goal of machine learning research is to produce methods that are applicable to highly complex tasks, such as perception (vision, audition), reasoning, intelligent control, and other...
To appear in “Large-Scale Kernel Machines”, (2007)
Yoshua Bengio, Yann Lecun, L. Bottou, O. Chapelle, ...
One long-term goal of machine learning research is to produce methods that are applicable to highly complex tasks, such as perception (vision, audition), reasoning, intelligent control, and other...
Inference with the Universum (2006)
Weston, J., Collobert, R., Sinz, F., Bottou, L., Vapnik, V.
We study classification tasks where one is given a set of labeled examples, and a set of "non-examples" of meaningful concepts in the same domain that do not belong to either class (refered to as the...
Trading Convexity for Scalability (2006)
Collobert, R., Sinz, F., Weston, J., Bottou, L.
Convex learning algorithms, such as Support Vector Machines (SVMs), are often seen as highly desirable because they offer strong practical properties and are amenable to theoretical analysis....
Large Scale Transductive SVMs (2006)
Collobert, R., Sinz, F., Weston, J., Bottou, L.
We show how the Concave-Convex Procedure can be applied to the optimization of Transductive SVMs, which traditionally requires solving a combinatorial search problem. This provides for the first time...
Toward automatic phenotyping of developing embryos from videos (2005)
Ning, F., Delhomme, D., Lecun, Y., Piano, F., Bottou, L., Barbano, P.E.
We describe a trainable system for analyzing videos of developing C. elegans embryos. The system automatically detects, segments, and locates cells and nuclei in microscopic images. The system was...
Methods Towards Invasive Human Brain Computer Interfaces (2005)
Lal, T.N., Hinterberger, T., Widman, G., Schröder, M., Hill, J., Rosenstiel, W., ...
During the last ten years there has been growing interest in the development of Brain Computer Interfaces (BCIs). The field has mainly been driven by the needs of completely paralyzed patients to...
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...
Semi-supervised Learning on Directed Graphs (2005)
Zhou, D., Schölkopf, B., Hofmann, T., Saul, L.K., Weiss, Y., Bottou, L.
Given a directed graph in which some of the nodes are labeled, we investigate the question of how to exploit the link structure of the graph to infer the labels of the remaining unlabeled nodes. To...
Implicit Wiener series for higher-order image analysis (2005)
Franz, M.O., Schölkopf, B., Saul, L.K., Weiss, Y., Bottou, L.
The computation of classical higher-order statistics such as higher-order moments or spectra is difficult for images due to the huge number of terms to be estimated and interpreted. We propose an...
Machine Learning Applied to Perception: Decision Images for Classification (2005)
Wichmann, F.A., Graf, A.B.A., Simoncelli, E.P., Bülthoff, H.H., Schölkopf, B., Saul, L. K., ...
We study gender discrimination of human faces using a combination of psychophysical classification and discrimination experiments together with methods from machine learning. We reduce the...
Matrix Exponential Updates for On-line Learning and Bregman Projection (2005)
Tsuda, K., Rätsch, G., Warmuth, M.K., Saul, L.K., Weiss, Y., Bottou, L.
A Machine Learning Approach to Conjoint Analysis (2005)
Chapelle, O., Harchaoui, Z., Saul, L.K., Weiss, Y., Bottou, L.
Choice-based conjoint analysis builds models of consumers preferences over products with answers gathered in questionnaires. Our main goal is to bring tools from the machine learning community to...
An Auditory Paradigm for Brain--Computer Interfaces (2005)
Hill, N.J., Lal, T.N., Bierig, K., Birbaumer, N., Schölkopf, B., Saul, L.K., ...
Motivated by the particular problems involved in communicating with "locked-in" paralysed patients, we aim to develop a brain-computer interface that uses auditory stimuli. We describe a paradigm...
Kernel Methods for Implicit Surface Modeling (2005)
Schölkopf, B., Giesen, J., Spalinger, S., Saul, L.K., Weiss, Y., Bottou, L., ...
We describe methods for computing an implicit model of a hypersurface that is given only by a finite sampling. The methods work by mapping the sample points into a reproducing kernel Hilbert space...
Toward automatic phenotyping of developing embryos from videos (2005)
Ning, F., Delhomme, D., Lecun, Y., Piano, F., Bottou, L., Barbano, P.E.
We describe a trainable system for analyzing videos of developing C. elegans embryos. The system automatically detects, segments, and locates cells and nuclei in microscopic images. The system was...
L.: A general segmentation scheme for DjVu document compression (2002)
P. Haffner, L. Bottou, Y. Lecun, L. Vincent
image compression methodology, a file format, and a delivery platform that together, enable instant access to high quality documents from essentially any platform, over any connection. Originally...
Support Vector Machine - Reference Manual (1998)
Saunders, C., Stitson, M.O., Weston, J., Bottou, L., Schoelkopf., B., Smola, A.
Support Vector Machine - Reference Manual (1998)
Saunders, C., Stitson, M.O., Weston, J., Bottou, L., Schoelkopf., B., Smola, A.
Support Vector Machine - Reference Manual (1998)
Saunders, C., Stitson, M.O., Weston, J., Bottou, L., Schoelkopf., B., Smola, A.
Support vector machine reference manual (1998)
C. Saunders, M. O. Stitson, J. Weston, Royal Holloway, L. Bottou, B. Scholkopf, ...
The Support Vector Machine (SVM) is a new type of learning machine. The SVM is a general architecture that can be applied to pattern recognition, regression estimation and other problems. The...
Nonlinear component analysis as a kernel eigenvalue problem (1998)
Bernhard Scholkopf, Er Smola, L. Bottou, C. Burges, H. Bultho, K. Gegenfurtner, ...
We describe a new method for performing a nonlinear form of Principal Component Analysis. By the use of integral operator kernel functions, we can e ciently compute principal components in...
Support Vector Machine - Reference Manual (1998)
C. Saunders, M. O. Stitson, J. Weston, Royal Holloway, L. Bottou, B. Schölkopf, ...
this document will describe these programs. To find out more about SVMs, see the bibliography. We will not describe how SVMs work here.
Support Vector Machine - Reference Manual (1998)
C. Saunders, M. O. Stitson, J. Weston, Royal Holloway, L. Bottou, B. Scholkopf, ...
this document will describe these programs. To find out more about SVMs, see the bibliography. We will not describe how SVMs work here.
Nonlinear Component Analysis as a Kernel Eigenvalue Problem (1998)
F��r Biologische Kybernetik, Alexander Smola, Bernhard Schölkopf, Er Smola, Klaus-Robert Müller, L. Bottou, ...
We describe a new method for performing a nonlinear form of Principal Component Analysis. By the use of integral operator kernel functions, we can efficiently compute principal components in...
Comparison of learning algorithms for handwritten digit recognition (1995)
C. Cartes, E. Ssckinger, Y. Lecun, Y. Lecun, L. Jackel, L. Jackel, ...
This paper compares the performance of several classifier algorithms on a standard database of handwritten digits. We consider not only raw accuracy, but also rejection, training time, recognition...
Comparison of learning algorithms for handwritten digit recognition (1995)
Y. Lecun, Y. Lecun, L. Jackel, L. Jackel, L. Bottou, L. Bottou, ...
This paper compares the performance of several classi�er algorithms on a standard database of handwritten digits. We consider not only raw accuracy, but also rejection, training time, recognition...
Comparison Of Learning Algorithms For Handwritten Digit Recognition (1995)
Y. LeCun, Y. Lecun, L. Jackel, L. Jackel, L. Bottou, L. Bottou, ...
This paper compares the performance of several classifier algorithms on a standard database of handwritten digits. We consider not only raw accuracy, but also rejection, training time, recognition...
Comparison Of Learning Algorithms For Handwritten Digit Recognition (1995)
Lecun Jackel Bottou, Y. Lecun, Y. Lecun, L. Jackel, L. Jackel, L. Bottou, ...
This paper compares the performance of several classifier algorithms on a standard database of handwritten digits. We consider not only raw accuracy, but also rejection, training time, recognition...