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Your search keyword '"Chang, Lei"' showing total 35 results
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35 results on '"Chang, Lei"'

3. ‘Mixed Blessings’

Author

Bornstein, Marc H., primary, Putnick, Diane L., additional, Lansford, Jennifer E., additional, Al-Hassan, Suha M., additional, Bacchini, Dario, additional, Bombi, Anna Silvia, additional, Pastorelli, Concetta, additional, Chang, Lei, additional, Deater-Deckard, Kirby, additional, Di Giunta, Laura, additional, Dodge, Kenneth A., additional, Malone, Patrick S., additional, Skinner, Ann T., additional, Oburu, Paul, additional, Sorbring, Emma, additional, Steinberg, Laurence, additional, Tapanya, Sombat, additional, Uribe Tirado, Liliana Maria, additional, Zelli, Arnaldo, additional, and Alampay, Liane Peña, additional

Published
2022
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6. Achieving the Sustainable Development Goals

Author

Lansford, Jennifer E., primary, Rothenberg, W. Andrew, additional, Tapanya, Sombat, additional, Tirado, Liliana Maria Uribe, additional, Yotanyamaneewong, Saengduean, additional, Alampay, Liane Peña, additional, M. Al-Hassan, Suha, additional, Bacchini, Dario, additional, H. Bornstein, Marc, additional, Chang, Lei, additional, Deater-Deckard, Kirby, additional, Di Giunta, Laura, additional, A. Dodge, Kenneth, additional, Gurdal, Sevtap, additional, Liu, Qin, additional, Long, Qian, additional, S. Malone, Patrick, additional, Oburu, Paul, additional, Pastorelli, Concetta, additional, T. Skinner, Ann, additional, Sorbring, Emma, additional, and Steinberg, Laurence, additional

Published
2021
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8. International Collaboration in the Study of Positive Youth Development

Author

Lansford, Jennifer E., primary, Chang, Lei, additional, Deater-Deckard, Kirby, additional, Di Giunta, Laura, additional, Dodge, Kenneth A., additional, Malone, Patrick S., additional, Oburu, Paul, additional, Pastorelli, Concetta, additional, Skinner, Ann T., additional, Sorbring, Emma, additional, Steinberg, Laurence, additional, Tapanya, Sombat, additional, Uribe Tirado, Liliana Maria, additional, Alampay, Liane Peña, additional, Al-Hassan, Suha M., additional, Bacchini, Dario, additional, and Bornstein, Marc H., additional

Published
2021
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23. Parenting and Positive Adjustment for Adolescents in Nine Countries

Author

Lansford, Jennifer E., primary, Al-Hassan, Suha M., additional, Bacchini, Dario, additional, Bornstein, Marc H., additional, Chang, Lei, additional, Chen, Bin-Bin, additional, Deater-Deckard, Kirby, additional, Di Giunta, Laura, additional, Dodge, Kenneth A., additional, Malone, Patrick S., additional, Oburu, Paul, additional, Skinner, Ann T., additional, Pastorelli, Concetta, additional, Sorbring, Emma, additional, Steinberg, Laurence, additional, Icenogle, Grace, additional, Tapanya, Sombat, additional, Alampay, Liane Peña, additional, Uribe Tirado, Liliana Maria, additional, and Zelli, Arnaldo, additional

Published
2017
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33. Aggregation Models for People Finding in Enterprise Corpora.

Author

Zhang, Wei, Chang, Lei, Ma, Jianqing, and Zhong, Yiping

Abstract

Finding authoritative people of a given field automatically within a large organization is quite helpful in various aspects, such as problem consulting and team building. In this paper, a novel aggregation model is proposed to solve the problem of finding authoritative people. Various kinds of related information in an enterprise repository is assembled to model the knowledge and skills of a candidate, for instance, such information may be the profile which gives a personal description of the candidate, documents related with the candidate, people with similar intellectual structure and so on. Then the candidate is modeled as a multinomial probability distribution over these collected evidence of expertise and candidates are ranked according to the probability of the topic generated by their models. Experimental results on TREC benchmark enterprise corpora demonstrate that our model outperforms current state-of-the-art approaches by a large margin. [ABSTRACT FROM AUTHOR]

Published
2009
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34. BOAI: Fast Alternating Decision Tree Induction Based on Bottom-Up Evaluation.

Author

Yang, Bishan, Wang, Tengjiao, Yang, Dongqing, and Chang, Lei

Abstract

Alternating Decision Tree (ADTree) is a successful classification model based on boosting and has a wide range of applications. The existing ADTree induction algorithms apply a ˵top-down″ strategy to evaluate the best split at each boosting iteration, which is very time-consuming and thus is unsuitable for modeling on large data sets. This paper proposes a fast ADTree induction algorithm (BOAI) based on ˵bottom-up″ evaluation, which offers high performance on massive data without sacrificing classification accuracy. BOAI uses a pre-sorting technique and dynamically evaluates splits by a bottom-up approach based on VW-group. With these techniques, huge redundancy in sorting and computation can be eliminated in the tree induction procedure. Experimental results on both real and synthetic data sets show that BOAI outperforms the best existing ADTree induction algorithm by a significant margin. In the real case study, BOAI also provides better performance than TreeNet and Random Forests, which are considered as efficient classification models. [ABSTRACT FROM AUTHOR]

Published
2008
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35. Mining Compressed Sequential Patterns.

Author

Li, Xue, Zaïane, Osmar R., Li, Zhanhuai, Chang, Lei, Yang, Dongqing, Tang, Shiwei, and Wang, Tengjiao

Abstract

Current sequential pattern mining algorithms often produce a large number of patterns. It is difficult for a user to explore in so many patterns and get a global view of the patterns and the underlying data. In this paper, we examine the problem of how to compress a set of sequential patterns using only K SP-Features(Sequential Pattern Features). A novel similarity measure is proposed for clustering SP-Features and an effective SP-Feature combination method is designed. We also present an efficient algorithm, called CSP(Compressing Sequential Patterns) to mine compressed sequential patterns based on the hierarchical clustering framework. A thorough experimental study with both real and synthetic datasets shows that CSP can compress sequential patterns effectively. [ABSTRACT FROM AUTHOR]

Published
2006
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