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"... Combined results on b-hadron lifetimes, b-hadron production rates, B 0 d - B 0 d and B 0 s - B 0 s oscillations, the decay width di#erence between the mass eigenstates of the B 0 s - B 0 s system, and the values of the CKM matrix elements |V cb | and |V ub | are obtained from pu ..."
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Combined results on b-hadron lifetimes, b-hadron production rates, B 0 d - B 0 d and B 0 s - B 0 s oscillations, the decay width di#erence between the mass eigenstates of the B 0 s - B 0 s system, and the values of the CKM matrix elements |V cb | and |V ub | are obtained from
An Efficient Boosting Algorithm for Combining Preferences
, 1999
"... The problem of combining preferences arises in several applications, such as combining the results of different search engines. This work describes an efficient algorithm for combining multiple preferences. We first give a formal framework for the problem. We then describe and analyze a new boosting ..."
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Cited by 727 (18 self)
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The problem of combining preferences arises in several applications, such as combining the results of different search engines. This work describes an efficient algorithm for combining multiple preferences. We first give a formal framework for the problem. We then describe and analyze a new
Combining labeled and unlabeled data with co-training
, 1998
"... We consider the problem of using a large unlabeled sample to boost performance of a learning algorithm when only a small set of labeled examples is available. In particular, we consider a setting in which the description of each example can be partitioned into two distinct views, motivated by the ta ..."
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Cited by 1633 (28 self)
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provide empirical results on real web-page data indicating that this use of unlabeled examples can lead to signi cant improvement of hypotheses in practice. As part of our analysis, we provide new re-
Cluster Ensembles - A Knowledge Reuse Framework for Combining Multiple Partitions
- Journal of Machine Learning Research
, 2002
"... 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 determined these partitionings. We first identify several application scenarios for the resultant 'knowledge reuse&ap ..."
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Cited by 603 (20 self)
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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 determined these partitionings. We first identify several application scenarios for the resultant 'knowledge reuse
Combination of Multiple Searches
- THE SECOND TEXT RETRIEVAL CONFERENCE (TREC-2
, 1994
"... The TREC-3 project at Virginia Tech focused on methods for combining the evidence from multiple retrieval runs and queries to improve retrieval performance over any single retrieval method or query. The largest improvements result from the combination of retrieval paradigms rather than from the use ..."
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Cited by 437 (2 self)
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The TREC-3 project at Virginia Tech focused on methods for combining the evidence from multiple retrieval runs and queries to improve retrieval performance over any single retrieval method or query. The largest improvements result from the combination of retrieval paradigms rather than from the use
Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms
, 2002
"... We describe new algorithms for training tagging models, as an alternative to maximum-entropy models or conditional random fields (CRFs). The algorithms rely on Viterbi decoding of training examples, combined with simple additive updates. We describe theory justifying the algorithms through a modific ..."
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Cited by 660 (13 self)
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We describe new algorithms for training tagging models, as an alternative to maximum-entropy models or conditional random fields (CRFs). The algorithms rely on Viterbi decoding of training examples, combined with simple additive updates. We describe theory justifying the algorithms through a
Exploiting Generative Models in Discriminative Classifiers
- In Advances in Neural Information Processing Systems 11
, 1998
"... Generative probability models such as hidden Markov models provide a principled way of treating missing information and dealing with variable length sequences. On the other hand, discriminative methods such as support vector machines enable us to construct flexible decision boundaries and often resu ..."
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Cited by 551 (9 self)
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result in classification performance superior to that of the model based approaches. An ideal classifier should combine these two complementary approaches. In this paper, we develop a natural way of achieving this combination by deriving kernel functions for use in discriminative methods such as support
Boosting a Weak Learning Algorithm By Majority
, 1995
"... We present an algorithm for improving the accuracy of algorithms for learning binary concepts. The improvement is achieved by combining a large number of hypotheses, each of which is generated by training the given learning algorithm on a different set of examples. Our algorithm is based on ideas pr ..."
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Cited by 516 (16 self)
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upper bounds known today. We show that the number of hypotheses that are combined by our algorithm is the smallest number possible. Other outcomes of our analysis are results regarding the representational power of threshold circuits, the relation between learnability and compression, and a method
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