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pro vyhledávání: '"Ralf Herbrich"'
Autor:
Ralf Herbrich
An overview of the theory and application of kernel classification methods.Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier—a limited, but well-
Publikováno v:
Proceedings of the International AAAI Conference on Web and Social Media. 6:443-446
Traditionally, social network analyses are applied to data from a particular social domain. With the advent of online social networks such as Facebook, we observe an aggregate of various social domains resulting in a layered mix of professional conta
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::939ed5e07a976104c0b632e815bb1950
https://doi.org/10.1609/icwsm.v6i1.14337
https://doi.org/10.1609/icwsm.v6i1.14337
Autor:
Sergio Escalera, Ralf Herbrich
Publikováno v:
The NeurIPS '18 Competition ISBN: 9783030291341
Competitions have become an integral part of advancing state-of-the-art in artificial intelligence (AI). They exhibit one important difference to benchmarks: Competitions test a system end-to-end rather than evaluating only a single component; they a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8c19c4049d60cb2be6f4b63792a86ba7
https://doi.org/10.1007/978-3-030-29135-8_1
https://doi.org/10.1007/978-3-030-29135-8_1
Autor:
Sergio Escalera, Ralf Herbrich
This volume presents the results of the Neural Information Processing Systems Competition track at the 2018 NeurIPS conference. The competition follows the same format as the 2017 competition track for NIPS. Out of 21 submitted proposals, eight compe
Autor:
Ralf Herbrich
Publikováno v:
Künstliche Intelligenz ISBN: 9783662575673
Amazon forscht und entwickelt intensiv an Kunstlicher Intelligenz (KI), insbesondere im Bereich des Maschinellen Lernens. Die Ergebnisse der Arbeit kommen schon heute den Kunden zugute – etwa indem Systeme auf Grundlage von Daten aus der Vergangenh
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c90266d87f39f32720466b496cebdedd
https://doi.org/10.1007/978-3-662-57568-0_4
https://doi.org/10.1007/978-3-662-57568-0_4
Autor:
Ralf Herbrich
Publikováno v:
WSDM
In this talk I will give an introduction into the field of machine learning and discuss why it is a crucial technology for Amazon. Machine learning is the science of automatically extracting patterns from data in order to make automated predictions o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6310487b9a1b1f4a0ddff9ac9e3eb955
https://doi.org/10.1145/3018661.3022764
https://doi.org/10.1145/3018661.3022764
Autor:
Simon Price, Bruno Golenia, Sebastian Spiegler, John Guiver, Peter A. Flach, Mohammed J. Zaki, Thore Graepel, Ralf Herbrich
Publikováno v:
ACM SIGKDD Explorations Newsletter. 11:63-67
The SIGKDD'09 Research Track received 537 paper submissions, which were reviewed by a Program Committee of 199 members, and a Senior Program Committee of 22 members. We used techniques from artificial intelligence and data mining to streamline and su
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a791846be7d5508981f90620c745817e
https://doi.org/10.1145/1809400.1809413
https://doi.org/10.1145/1809400.1809413
Publikováno v:
Machine Learning. 59:55-76
We consider bounds on the prediction error of classification algorithms based on sample compression. We refine the notion of a compression scheme to distinguish permutation and repetition invariant and non-permutation and repetition invariant compres
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a93588e0c479753509fc6c9cc7fd0c2f
https://doi.org/10.1007/s10994-005-0462-7
https://doi.org/10.1007/s10994-005-0462-7
Autor:
Thore Graepel, Ralf Herbrich
Publikováno v:
IEEE Transactions on Information Theory. 48:3140-3150
We present a bound on the generalization error of linear classifiers in terms of a refined margin quantity on the training sample. The result is obtained in a probably approximately correct (PAC)-Bayesian framework and is based on geometrical argumen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5bdb534c91ac88235cdaa8a266a56a1d
https://doi.org/10.1109/tit.2002.805090
https://doi.org/10.1109/tit.2002.805090
Autor:
Tao Xu, Stuart Michael Bowers, Antoine Joseph Atallah, Joaquin Quinonero Candela, Bo Liu, Ralf Herbrich, Ou Jin, Junfeng Pan, Yanxin Shi, Xinran He, Tianbing Xu
Publikováno v:
ADKDD@KDD
Online advertising allows advertisers to only bid and pay for measurable user responses, such as clicks on ads. As a consequence, click prediction systems are central to most online advertising systems. With over 750 million daily active users and ov
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3cbf3a948737a524c21d7171a687f3dd
https://doi.org/10.1145/2648584.2648589
https://doi.org/10.1145/2648584.2648589