J Kittler, F Roli - Soft computing approach to pattern recognition …, 2002 - books.google.com Chapter 1 Multiple Classifier Systems J. Kittler Centre for Vision, Speech and Signal Processing
School of Electronics and Physical Sciences University of Surrey Guildford GU2 7XH, United
Kingdom E-mail: J. Kittler@ eim. surrey. ac. uk Over the last decade, multiple classifier ... Cited by 69 - Related articles - All 2 versions
psu.edu[PDF]T Dietterich - Multiple classifier systems, 2000 - Springer Abstract. Ensemble methods are learning algorithms that construct a set of classifiers and then
classify new data points by taking a (weigh- ted) vote of their predictions. The original ensemble
method is Bayesian averaging, but more recent algorithms include error-correcting output ... Cited by 1280 - Related articles - BL Direct - All 41 versions
unina.it[PDF]F Roli, J Kittler, T Windeatt - 2002 - amalfi.dis.unina.it Josef Kittler University of Surrey Centre for Vision, Speech and Signal Processing Guildford GU2
7XH, United Kingdom E-mail: j.kittler@eim.surrey.ac.uk Fabio Roli University of Cagliari Department
of Electrical and Electronic Engineering Piazza D'Armi, 09123 Cagliari, Italy E-mail: roli@ ... Cited by 35 - Related articles - View as HTML - Library Search - All 4 versions
scientificcommons.org[HTML]J Kittler - Pattern Analysis & Applications, 1998 - Springer Abstract: The problem of classifier combination is considered in the context of the two main fusion
scenarios: fusion of opinions based on identical and on distinct representations. We develop
a theoretical framework for classifier combination for these two scenarios. For multiple ... Cited by 2699 - Related articles - BL Direct - All 15 versions
psu.edu[PDF]MI Jordan, RA Jacobs - Neural computation, 1994 - MIT Press We present a tree-structured architecture for supervised learning. The statistical model underlying
the architecture is a hierarchical mixture model in which both the mixture coefficients and the
mixture compo- nents are generalized linear models (GLIM's). Learning is treated as a ... Cited by 1827 - Related articles - BL Direct - All 75 versions
psu.edu[PDF]TG Dietterich - Machine learning, 2000 - Springer Abstract. Bagging and boosting are methods that generate a diverse ensemble of classifiers
by manipulating the training data given to a “base” learning algorithm. Breiman has pointed out
that they rely for their effectiveness on the instability of the base learning algorithm. An ... Cited by 1004 - Related articles - BL Direct - All 33 versions
martinsewell.com[PDF]LK Hansen, P Salamon - IEEE Transactions on Pattern …, 1990 - ieeexplore.ieee.org Manuscript received March 20, 1989; revised March 14, 1990. Rec- ommended for acceptance
by R. De Mon. The work of LK Hansen was supported by the Danish Teknologiradet. The work
of P. Salamon was sup- ported by the Naval Ocean Systems Center under Contract ... Cited by 1546 - Related articles - All 14 versions
psu.edu[PDF]F Roli, G Giacinto, G Vernazza - Multiple Classifier Systems, 2001 - Springer Abstract. In the field of pattern recognition, multiple classifier systems based on the combination
of outputs of a set of different classifiers have been proposed as a method for the development
of high performance classification systems. In this paper, the problem of design of multiple ... Cited by 92 - Related articles - BL Direct - All 12 versions
psu.edu[PDF]A Krogh, J Vedelsby - Advances in neural information processing systems, 1995 - Citeseer Learning of continuous valued functions using neural network en- sembles (committees) can
give improved accuracy, reliable estima- tion of the generalization error, and active learning.
The ambiguity is de ned as the variation of the output of ensemble members aver- aged ... Cited by 951 - Related articles - View as HTML - BL Direct - All 17 versions
psu.edu[PDF]A Strehl, J Ghosh - The Journal of Machine Learning Research, 2003 - portal.acm.org This paper introduces the problem of combining multiple partitionings of a set of objects into
a single consolidated clustering without accessing the features or algorithms that deter- mined
these partitionings. We first identify several application scenarios for the resultant ' ... Cited by 828 - Related articles - BL Direct - All 41 versions
