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AdaRank: a boosting algorithm for information retrieval. AdaRank: a boosting algorithm for information retrieval," Jun Xu, Hang Li, SIGIR 2007. This paper proposes an boosting algorithm AdaRank. Of the issue learning to rank. For document retrieval. According to the essence of AdaBoost, AdaRank repeatedly constructs "weak rankers" on the basis of re-weighted data, and the final prediction function is also the linear combination of those "weak rankers". The evaluation of AdaRank. Based on Adaboost, AdaRank.

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aMMAI | lmyammai.blogspot.com Reviews

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AdaRank: a boosting algorithm for information retrieval. AdaRank: a boosting algorithm for information retrieval, Jun Xu, Hang Li, SIGIR 2007. This paper proposes an boosting algorithm AdaRank. Of the issue learning to rank. For document retrieval. According to the essence of AdaBoost, AdaRank repeatedly constructs weak rankers on the basis of re-weighted data, and the final prediction function is also the linear combination of those weak rankers. The evaluation of AdaRank. Based on Adaboost, AdaRank.

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aMMAI | lmyammai.blogspot.com Reviews

https://lmyammai.blogspot.com

AdaRank: a boosting algorithm for information retrieval. AdaRank: a boosting algorithm for information retrieval," Jun Xu, Hang Li, SIGIR 2007. This paper proposes an boosting algorithm AdaRank. Of the issue learning to rank. For document retrieval. According to the essence of AdaBoost, AdaRank repeatedly constructs "weak rankers" on the basis of re-weighted data, and the final prediction function is also the linear combination of those "weak rankers". The evaluation of AdaRank. Based on Adaboost, AdaRank.

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aMMAI: 六月 2009

http://lmyammai.blogspot.com/2009_06_01_archive.html

AdaRank: a boosting algorithm for information retrieval. AdaRank: a boosting algorithm for information retrieval," Jun Xu, Hang Li, SIGIR 2007. This paper proposes an boosting algorithm AdaRank. Of the issue learning to rank. For document retrieval. According to the essence of AdaBoost, AdaRank repeatedly constructs "weak rankers" on the basis of re-weighted data, and the final prediction function is also the linear combination of those "weak rankers". The evaluation of AdaRank. Based on Adaboost, AdaRank.

2

aMMAI: Nonlinear dimensionality reduction by locally linear embedding

http://lmyammai.blogspot.com/2009/03/nonlinear-dimensionality-reduction-by.html

Nonlinear dimensionality reduction by locally linear embedding. Nonlinear dimensionality reduction by locally linear embedding," Roweis and Saul, Science, 2000. This paper present an unsupervised learning algorithm, locally linear embedding. LLE), which can construct a neighborhood preserving mapping based on the steps below:. Assign K neighbors for each data point X i. From its neighbors with linear weight, by minimize the cost function. Compute the low-dimensional embedding vector Y i.

3

aMMAI: [Note] 20090325

http://lmyammai.blogspot.com/2009/03/note-20090325.html

Feature significance (without label / subset). Feature significance (with label / subset). MI, x 2, correlation (subset). Exploiting feature correlation (without label / transform). PCA, manifold methods (LLE, ISOMAP). Exploiting feature correlation (with label / transform). Adaboost, maximum entropy. Exploiting hidden semantics (topics). PLSA, SVD, LSI, Information Bottleneck. 張貼者： LMY (for ammai). 標籤： Class note. 訂閱：張貼留言 (Atom). Paper critiques and summaries.

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aMMAI: AdaRank: a boosting algorithm for information retrieval

http://lmyammai.blogspot.com/2009/06/adarank-boosting-algorithm-for.html

AdaRank: a boosting algorithm for information retrieval. AdaRank: a boosting algorithm for information retrieval," Jun Xu, Hang Li, SIGIR 2007. This paper proposes an boosting algorithm AdaRank. Of the issue learning to rank. For document retrieval. According to the essence of AdaBoost, AdaRank repeatedly constructs "weak rankers" on the basis of re-weighted data, and the final prediction function is also the linear combination of those "weak rankers". The evaluation of AdaRank. Based on Adaboost, AdaRank.

5

aMMAI: 二月 2009

http://lmyammai.blogspot.com/2009_02_01_archive.html

How to give a good research talk. How to give a good research talk. Jones et. al. As a new graduate, I have no experience to present my work at the stage since I do not have work. I only have the experience to present other person's work at meeting. Therefore, I feel hard to understand some suggestion of the paper such as don't put outlines in the start of the slides, don't start preparing slides early. I summarize the paper as below. How to prepare slides? Use some aids such as overhead projector. Howev...

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aMMAI

AdaRank: a boosting algorithm for information retrieval. AdaRank: a boosting algorithm for information retrieval," Jun Xu, Hang Li, SIGIR 2007. This paper proposes an boosting algorithm AdaRank. Of the issue learning to rank. For document retrieval. According to the essence of AdaBoost, AdaRank repeatedly constructs "weak rankers" on the basis of re-weighted data, and the final prediction function is also the linear combination of those "weak rankers". The evaluation of AdaRank. Based on Adaboost, AdaRank.

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