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The Skill Content of Recent Technological Change: An Empirical Exploration

by David H. Autor, Frank Levy, Richard J. Murnane , 2000
"... Recent empirical and case study evidence documents a strong association between the adoption of computers and increased use of college educated or non-production workers. With few exceptions, the conceptual link explaining how computer technology complements skilled labor or substitutes for unskille ..."
Abstract - Cited by 643 (28 self) - Add to MetaCart
Recent empirical and case study evidence documents a strong association between the adoption of computers and increased use of college educated or non-production workers. With few exceptions, the conceptual link explaining how computer technology complements skilled labor or substitutes

Improving generalization with active learning

by David Cohn, Richard Ladner, Alex Waibel - Machine Learning , 1994
"... Abstract. Active learning differs from "learning from examples " in that the learning algorithm assumes at least some control over what part of the input domain it receives information about. In some situations, active learning is provably more powerful than learning from examples ..."
Abstract - Cited by 544 (1 self) - Add to MetaCart
alone, giving better generalization for a fixed number of training examples. In this article, we consider the problem of learning a binary concept in the absence of noise. We describe a formalism for active concept learning called selective sampling and show how it may be approximately implemented by a

Improving retrieval performance by relevance feedback

by Gerard Salton, Chris Buckley - Journal of the American Society for Information Science , 1990
"... Relevance feedback is an automatic process, introduced over 20 years ago, designed to produce improved query formulations following an initial retrieval operation. The principal relevance feedback methods described over the years are examined briefly, and evaluation data are included to demonstrate ..."
Abstract - Cited by 756 (6 self) - Add to MetaCart
Relevance feedback is an automatic process, introduced over 20 years ago, designed to produce improved query formulations following an initial retrieval operation. The principal relevance feedback methods described over the years are examined briefly, and evaluation data are included to demonstrate

Dropout from higher education: A theoretical synthesis of recent research

by Vincent Tinto - Review of Educational Research , 1975
"... Despite the very extensive literature on dropout from higher education, much remains unknown about the nature of the dropout process. In large measure, the failure of past research to delineate more clearly the multiple characteristics of dropout can be traced to two major shortcomings; namely, inad ..."
Abstract - Cited by 798 (2 self) - Add to MetaCart
, inadequate atten-tion given to questions of definition and to the development of theoretical models that seek to explain, not simply to describe, the processes that bring individuals to leave institutions of higher education. With regard to the former, inadequate attention given to definition has often led

SPEA2: Improving the Strength Pareto Evolutionary Algorithm

by Eckart Zitzler, Marco Laumanns, Lothar Thiele , 2001
"... The Strength Pareto Evolutionary Algorithm (SPEA) (Zitzler and Thiele 1999) is a relatively recent technique for finding or approximating the Pareto-optimal set for multiobjective optimization problems. In different studies (Zitzler and Thiele 1999; Zitzler, Deb, and Thiele 2000) SPEA has shown very ..."
Abstract - Cited by 708 (19 self) - Add to MetaCart
The Strength Pareto Evolutionary Algorithm (SPEA) (Zitzler and Thiele 1999) is a relatively recent technique for finding or approximating the Pareto-optimal set for multiobjective optimization problems. In different studies (Zitzler and Thiele 1999; Zitzler, Deb, and Thiele 2000) SPEA has shown

Improved Boosting Algorithms Using Confidence-rated Predictions

by Robert E. Schapire , Yoram Singer - MACHINE LEARNING , 1999
"... We describe several improvements to Freund and Schapire’s AdaBoost boosting algorithm, particularly in a setting in which hypotheses may assign confidences to each of their predictions. We give a simplified analysis of AdaBoost in this setting, and we show how this analysis can be used to find impr ..."
Abstract - Cited by 940 (26 self) - Add to MetaCart
We describe several improvements to Freund and Schapire’s AdaBoost boosting algorithm, particularly in a setting in which hypotheses may assign confidences to each of their predictions. We give a simplified analysis of AdaBoost in this setting, and we show how this analysis can be used to find

Mining Sequential Patterns: Generalizations and Performance Improvements

by Ramakrishnan Srikant, Rakesh Agrawal - RESEARCH REPORT RJ 9994, IBM ALMADEN RESEARCH , 1995
"... The problem of mining sequential patterns was recently introduced in [3]. We are given a database of sequences, where each sequence is a list of transactions ordered by transaction-time, and each transaction is a set of items. The problem is to discover all sequential patterns with a user-specified ..."
Abstract - Cited by 759 (5 self) - Add to MetaCart
The problem of mining sequential patterns was recently introduced in [3]. We are given a database of sequences, where each sequence is a list of transactions ordered by transaction-time, and each transaction is a set of items. The problem is to discover all sequential patterns with a user

Improved algorithms for optimal winner determination in combinatorial auctions and generalizations

by Tuomas Sandholm, Subhash Suri , 2000
"... Combinatorial auctions can be used to reach efficient resource and task allocations in multiagent systems where the items are complementary. Determining the winners is NP-complete and inapproximable, but it was recently shown that optimal search algorithms do very well on average. This paper present ..."
Abstract - Cited by 582 (53 self) - Add to MetaCart
Combinatorial auctions can be used to reach efficient resource and task allocations in multiagent systems where the items are complementary. Determining the winners is NP-complete and inapproximable, but it was recently shown that optimal search algorithms do very well on average. This paper

Improved prediction of signal peptides -- SignalP 3.0

by Jannick Dyrløv Bendtsen, Henrik Nielsen, Gunnar von Heijne, Søren Brunak - J. MOL. BIOL. , 2004
"... We describe improvements of the currently most popular method for prediction of classically secreted proteins, SignalP. SignalP consists of two different predictors based on neural network and hidden Markov model algorithms, where both components have been updated. Motivated by the idea that the cle ..."
Abstract - Cited by 654 (7 self) - Add to MetaCart
We describe improvements of the currently most popular method for prediction of classically secreted proteins, SignalP. SignalP consists of two different predictors based on neural network and hidden Markov model algorithms, where both components have been updated. Motivated by the idea

Improved methods for building protein models in electron density maps and the location of errors in these models. Acta Crystallogr. sect

by T. A. Jones, J. -y. Zou, S. W. Cowan, M. Kjeldgaard - A , 1991
"... Map interpretation remains a critical step in solving the structure of a macromolecule. Errors introduced at this early stage may persist throughout crystallo-graphic refinement and result in an incorrect struc-ture. The normally quoted crystallographic residual is often a poor description for the q ..."
Abstract - Cited by 1051 (9 self) - Add to MetaCart
for the quality of the model. Strategies and tools are described that help to alleviate this problem. These simplify the model-building process, quantify the goodness of fit of the model on a per-residue basis and locate possible errors in pep-tide and side-chain conformations.
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