Unsupervised statistical learning applied to experimental high-energy physics and related areas

Filho, Eduardo F. Simas; Seixas, J. M.

doi:10.1142/s0129183116300025

Cited by 3 publications

(1 citation statement)

References 48 publications

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“… Wade et al (2016) used the regularized RF to select the features of high dimensional shape data from subcortical brain surfaces. PCA is a kind of unsupervised learning feature extraction algorithm which maps high-dimensional data to low-dimensional space by linear projection and reduces the dimension of data sets ( Simas Filho and Seixas, 2016 ). Skala et al (2007) chose a method based on PCA to use the information inherent in the dose-volume histograms (DVH) to analyze after image-guided radiation therapy for prostate cancer.…”

Section: Introductionmentioning

confidence: 99%

RF-PCA: A New Solution for Rapid Identification of Breast Cancer Categorical Data Based on Attribute Selection and Feature Extraction

Bian

Zhou

et al. 2020

Front. Genet.

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Section: Introductionmentioning

confidence: 99%

RF-PCA: A New Solution for Rapid Identification of Breast Cancer Categorical Data Based on Attribute Selection and Feature Extraction

Bian

Zhou

et al. 2020

Front. Genet.

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Various Feature Extraction and Classification Techniques

Kaur

Sharma

2018

Lecture Notes in Electrical Engineering

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Personalized query suggestion based on user behavior

Chen

Hao

Shao

et al. 2018

Int. J. Mod. Phys. C

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Query suggestions help users refine their queries after they input an initial query. Previous work mainly concentrated on similarity-based and context-based query suggestion approaches. However, models that focus on adapting to a specific user (personalization) can help to improve the probability of the user being satisfied. In this paper, we propose a personalized query suggestion model based on users’ search behavior (UB model), where we inject relevance between queries and users’ search behavior into a basic probabilistic model. For the relevance between queries, we consider their semantical similarity and co-occurrence which indicates the behavior information from other users in web search. Regarding the current user’s preference to a query, we combine the user’s short-term and long-term search behavior in a linear fashion and deal with the data sparse problem with Bayesian probabilistic matrix factorization (BPMF). In particular, we also investigate the impact of different personalization strategies (the combination of the user’s short-term and long-term search behavior) on the performance of query suggestion reranking. We quantify the improvement of our proposed UB model against a state-of-the-art baseline using the public AOL query logs and show that it beats the baseline in terms of metrics used in query suggestion reranking. The experimental results show that: (i) for personalized ranking, users’ behavioral information helps to improve query suggestion effectiveness; and (ii) given a query, merging information inferred from the short-term and long-term search behavior of a particular user can result in a better performance than both plain approaches.

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Unsupervised statistical learning applied to experimental high-energy physics and related areas

Cited by 3 publications

References 48 publications

RF-PCA: A New Solution for Rapid Identification of Breast Cancer Categorical Data Based on Attribute Selection and Feature Extraction

RF-PCA: A New Solution for Rapid Identification of Breast Cancer Categorical Data Based on Attribute Selection and Feature Extraction

Various Feature Extraction and Classification Techniques

Personalized query suggestion based on user behavior

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