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40,517
Experiments with a New Boosting Algorithm
, 1996
"... In an earlier paper, we introduced a new “boosting” algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that consistently generates classifiers whose performance is a little better than random guessing. We also introduced the relate ..."
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Cited by 2213 (20 self)
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-learning benchmarks. In the second set of experiments, we studied in more detail the performance of boosting using a nearest-neighbor classifier on an OCR problem.
Myrinet: A Gigabit-per-Second Local Area Network
- IEEE Micro
, 1995
"... Abstract. Myrinet is a new type of local-area network (LAN) based on the technology used for packet communication and switching within "massivelyparallel processors " (MPPs). Think of Myrinet as an MPP message-passing network that can span campus dimensions, rather than as a wide-a ..."
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Cited by 1011 (0 self)
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. The Caltech Mosaic was an experiment to "push the envelope " of multicomputer design and programming toward a system with up to tens of thousands of small, single-chip nodes rather than hundreds of circuit-board-size nodes. The fine-grain multicomputer places more extreme demands
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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search strategies, each of which is a query expansion for a given domain. For this task, we compare the performance of RankBoost to the individual search strategies. The second experiment is a collaborative-filtering task for making movie recommendations. Here, we present results comparing Rank
Optimal Brain Damage
, 1990
"... We have used information-theoretic ideas to derive a class of practical and nearly optimal schemes for adapting the size of a neural network. By removing unimportant weights from a network, several improvements can be expected: better generalization, fewer training examples required, and improved sp ..."
Abstract
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Cited by 510 (5 self)
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speed of learning and/or classification. The basic idea is to use second-derivative information to make a tradeoff between network complexity and training set error. Experiments confirm the usefulness of the methods on a real-world application.
Real-time american sign language recognition using desk and wearable computer based video
- IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1998
"... We present two real-time hidden Markov model-based systems for recognizing sentence-level continuous American Sign Language (ASL) using a single camera to track the user’s unadorned hands. The first system observes the user from a desk mounted camera and achieves 92 percent word accuracy. The secon ..."
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Cited by 627 (26 self)
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. The second system mounts the camera in a cap worn by the user and achieves 98 percent accuracy (97 percent with an unrestricted grammar). Both experiments use a 40-word lexicon.
Robust Real-time Object Detection
- International Journal of Computer Vision
, 2001
"... This paper describes a visual object detection framework that is capable of processing images extremely rapidly while achieving high detection rates. There are three key contributions. The first is the introduction of a new image representation called the “Integral Image ” which allows the features ..."
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Cited by 1184 (4 self)
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used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features and yields extremely efficient classifiers [6]. The third contribution is a method for combining classifiers in a “cascade ” which allows
Measuring individual differences in implicit cognition: The implicit association test
- J PERSONALITY SOCIAL PSYCHOL 74:1464–1480
, 1998
"... An implicit association test (IAT) measures differential association of 2 target concepts with an attribute. The 2 concepts appear in a 2-choice task (e.g., flower vs. insect names), and the attribute in a 2nd task (e.g., pleasant vs. unpleasant words for an evaluation attribute). When instructions ..."
Abstract
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Cited by 1008 (63 self)
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experiments, the IAT was sensitive to (a) near-universal evaluative differences (e.g., flower vs. insect), (b) expected individual differences in evaluative associations (Japanese + pleasant vs. Korean + pleasant for Japanese vs. Korean subjects), and (c) consciously disavowed evaluative differences (Black
Predicting Internet Network Distance with Coordinates-Based Approaches
- In INFOCOM
, 2001
"... In this paper, we propose to use coordinates-based mechanisms in a peer-to-peer architecture to predict Internet network distance (i.e. round-trip propagation and transmission delay) . We study two mechanisms. The first is a previously proposed scheme, called the triangulated heuristic, which is bas ..."
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Cited by 631 (6 self)
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is based on relative coordinates that are simply the distances from a host to some special network nodes. We propose the second mechanism, called Global Network Positioning (GNP), which is based on absolute coordinates computed from modeling the Internet as a geometric space. Since end hosts maintain
Unrealistic optimism about future life events.
- Journal of Personality and Social Psychology,
, 1980
"... Two studies investigated the tendency of people to be unrealistically optimistic about future life events. In Study 1, 258 college students estimated how much their own chances of experiencing 42 events differed from the chances of their classmates. Overall, they rated their own chances to be above ..."
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Cited by 535 (0 self)
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to be above average for positive events and below average for negative events, ps<.001. Cognitive and motivational considerations led to predictions that degree of desirability, perceived probability, personal experience, perceived controllability, and stereotype salience would influence the amount
Stacked generalization
- NEURAL NETWORKS
, 1992
"... This paper introduces stacked generalization, a scheme for minimizing the generalization error rate of one or more generalizers. Stacked generalization works by deducing the biases of the generalizer(s) with respect to a provided learning set. This deduction proceeds by generalizing in a second sp ..."
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Cited by 731 (9 self)
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This paper introduces stacked generalization, a scheme for minimizing the generalization error rate of one or more generalizers. Stacked generalization works by deducing the biases of the generalizer(s) with respect to a provided learning set. This deduction proceeds by generalizing in a second
Results 1 - 10
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40,517