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Using sampled data and regression to merge search engine results

Luo Si, Jamie Callan
2002 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '02  
Documents sampled for creating resource descriptions can also be used to create a sample centralized index, and this index is a source of training data for adaptive results merging algorithms.  ...  This paper addresses the problem of merging results obtained from different databases and search engines in a distributed information retrieval environment.  ...  The regression algorithm handled this testbed far more effectively than the CORI results merging algorithm.  ... 
doi:10.1145/564376.564382 dblp:conf/sigir/SiC02 fatcat:3nettcn26nahhipmw64rdobtaq

Using sampled data and regression to merge search engine results

Luo Si, Jamie Callan
2002 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '02  
Documents sampled for creating resource descriptions can also be used to create a sample centralized index, and this index is a source of training data for adaptive results merging algorithms.  ...  This paper addresses the problem of merging results obtained from different databases and search engines in a distributed information retrieval environment.  ...  The regression algorithm handled this testbed far more effectively than the CORI results merging algorithm.  ... 
doi:10.1145/564379.564382 fatcat:tphmgjydvnggdddpthdfy6j55a

Mixture model with multiple centralized retrieval algorithms for result merging in federated search

Dzung Hong, Luo Si
2012 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval - SIGIR '12  
Based on this observation, this paper proposes a novel approach for result merging by utilizing multiple centralized retrieval algorithms.  ...  Result merging is an important research problem in federated search for merging documents retrieved from multiple ranked lists of selected information sources into a single list.  ...  In this paper, we propose a novel result merging algorithm that utilizes multiple centralized retrieval algorithms.  ... 
doi:10.1145/2348283.2348393 dblp:conf/sigir/HongS12 fatcat:whb7o6plxbbi5mc2lkek3knxja

The multiple outliers detection using agglomerative hierarchical methods in circular regression model

Siti Zanariah Satari, Nur Faraidah Muhammad Di, Roslinazairimah Zakaria
2017 Journal of Physics, Conference Series  
Two agglomerative hierarchical clustering algorithms for identifying multiple outliers in circular regression model have been developed in this study.  ...  The results show that the single-linkage method performs very well in detecting the multiple outliers with lower masking and swamping effects.  ...  These results clearly indicate that the proposed algorithm is applicable in detecting multiple outliers for circular regression model and perform better at low level of contamination or situated closer  ... 
doi:10.1088/1742-6596/890/1/012152 fatcat:ohrsl75bnngg5jgjhrtugp6qoi

Network-based logistic regression integration method for biomarker identification

Ke Zhang, Wei Geng, Shuqin Zhang
2018 BMC Systems Biology  
This motivates us to develop robust biomarker identification methods by integrating multiple datasets.  ...  Many mathematical and statistical models and algorithms have been proposed to do biomarker identification in recent years.  ...  Results In this section, we first evaluate the proposed integrative logistic regression model using simulation studies, we then apply the method to multiple gene expression datasets for studying breast  ... 
doi:10.1186/s12918-018-0657-8 pmid:30598085 pmcid:PMC6311907 fatcat:s62f2ykuvzehnohc7txtuw6ory

Predictive Hierarchical Clustering: Learning clusters of CPT codes for improving surgical outcomes [article]

Elizabeth C. Lorenzi, Stephanie L. Brown, Zhifei Sun, Katherine Heller
2017 arXiv   pre-print
Therefore, merges are chosen based on a Bayesian hypothesis test, which chooses pairings of the subgroups that result in the best model fit, as measured by held out predictive likelihoods.  ...  model.  ...  We learn a regression model for each potential merge on a training set using two-thirds of the data, then test each model on the held out third for evaluation in the testing set using predictive likelihoods  ... 
arXiv:1604.07031v2 fatcat:upwvgfvnpzfcrgiayylbog6oyy

Fuzzy and Possibilistic Clustering for Multiple Instance Linear Regression

Mohamed Trabelsi, Hichem Frigui
2018 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)  
In this paper, we present an algorithm that uses robust fuzzy clustering with an appropriate distance to learn multiple linear models from a noisy feature space simultaneously.  ...  We also use the possibilistic memberships to identify the optimal number of regression models. We evaluate our approach on a series of synthetic data sets.  ...  In this paper, we assume that the underlying regression model is linear and we use (1) to identify multiple linear models.  ... 
doi:10.1109/fuzz-ieee.2018.8491540 dblp:conf/fuzzIEEE/TrabelsiF18 fatcat:slzqxvudvfbixnhslovc2b6wcm

Clustering and Regression Techniques for Stock Prediction

B.S. Bini, Tessy Mathew
2016 Procedia Technology - Elsevier  
For prediction of future stock price multiple regression technique is used which helps the buyers and sellers to choose their companies from stock.  ...  Among the different clustering techniques experimented, partitioning technique and model based technique give high performance i.e. K-means and EM clustering algorithm respectively.  ...  In this work Multiple regression technique is used for predicting the future stock price Validation indexes for Clustering Algorithms Index measure helps to seek out the accuracy of result obtained.  ... 
doi:10.1016/j.protcy.2016.05.104 fatcat:5tvqf7h6vvetfbxl6t3jrbl3ny

Decision Stream: Cultivating Deep Decision Trees [article]

Dmitry Ignatov, Andrey Ignatov
2017 arXiv   pre-print
Our experimental results reveal that the proposed approach significantly outperforms the standard decision tree learning methods on both regression and classification tasks, yielding a prediction error  ...  Various modifications of decision trees have been extensively used during the past years due to their high efficiency and interpretability.  ...  Table 1 shows the results for single DS, DT and DS −merge models, where the last one denotes a DS with disabled merging phase.  ... 
arXiv:1704.07657v3 fatcat:c2bwttudtjaq3ij3rnmdjgx3ju

A weighted curve fitting method for result merging in federated search

Chuan He, Dzung Hong, Luo Si
2011 Proceedings of the 34th international ACM SIGIR conference on Research and development in Information - SIGIR '11  
Result merging is an important step in federated search to merge the documents returned from multiple source-specific ranked lists for a user query.  ...  Previous result merging methods such as Semi-Supervised Learning (SSL) and Sample-Agglomerate Fitting Estimate (SAFE) use regression methods to estimate global document scores from document ranks in individual  ...  Empirical results on two datasets have shown the effectiveness of the proposed results merging algorithm.  ... 
doi:10.1145/2009916.2010107 dblp:conf/sigir/HeHS11 fatcat:54nmsyqmljbi7lq47hgx4igt3m

Rainfall Spatial Estimations: A Review from Spatial Interpolation to Multi-Source Data Merging

Qingfang Hu, Zhe Li, Leizhi Wang, Yong Huang, Yintang Wang, Lingjie Li
2019 Water  
rainfall, and multi-source rainfall merging since 2000.  ...  In light of the information sources used in rainfall spatial estimation, this paper summarized the research progress in traditional spatial interpolation, remote sensing retrieval, atmospheric reanalysis  ...  Multiple Regression Multivariate regression models can be used to quantitatively estimate the rainfall distribution in space by establishing the linear or nonlinear response relationship between rainfall  ... 
doi:10.3390/w11030579 fatcat:awws4kfij5aqnbzyi5ampk3b4m

An integration resolution algorithm for mining multiple branches in version control systems

Alexander Tarvo, Thomas Zimmermann, Jacek Czerwonka
2011 2011 27th IEEE International Conference on Software Maintenance (ICSM)  
Collecting this data becomes a challenge if the system was developed using multiple code branches.  ...  In this paper we present an integration resolution algorithm that facilitates data collection across multiple code branches.  ...  Such data can be particularly useful for building predictive models, such as regression prediction model [7] .  ... 
doi:10.1109/icsm.2011.6080807 dblp:conf/icsm/TarvoZC11 fatcat:k2rrbc27lfep7b6sczyiyr5x6u

Robust result merging using sample-based score estimates

Milad Shokouhi, Justin Zobel
2009 ACM Transactions on Information Systems  
Robust result merging using sample-based score estimates. ACM Trans.  ...  This method requires no assumptions about properties such as the retrieval models used.  ...  We use the terminology as follows: -In data fusion algorithms, different retrieval models are used on a single collection. Results returned by different models are merged to produce the final list.  ... 
doi:10.1145/1508850.1508852 fatcat:m5ergw3svvaelncjjrtpm7suxi

Frequent Subgraph Summarization with Error Control [chapter]

Zheng Liu, Ruoming Jin, Hong Cheng, Jeffrey Xu Yu
2013 Lecture Notes in Computer Science  
model.  ...  To achieve a good summarization quality, our summarization framework allows users to specify an error tolerance σ, and our algorithms will discover k summarization templates in a top-down fashion and keep  ...  Summarization Algorithms Subgraph Summarization by Regression We propose to use a regression approach to frequent subgraph summarization.  ... 
doi:10.1007/978-3-642-38562-9_1 fatcat:htvi3siylbh4hctqpq3saihthi

A Method for Merging Similar Zones to Improve Intelligent Models for Real Estate Appraisal [chapter]

Tadeusz Lasota, Edward Sawiłow, Bogdan Trawiński, Marta Roman, Paulina Marczuk, Patryk Popowicz
2015 Lecture Notes in Computer Science  
The study proved the usefulness of merging of similar areas which resulted in better reliability and accuracy of predicted prices.  ...  The foundations of the method were verified by experimental testing the accuracy of the models devised for the prediction of real estate prices built over the merged zones.  ...  and multiple models built using various resampling techniques [11] , [12] , [13] , [14] , [15] .  ... 
doi:10.1007/978-3-319-15702-3_46 fatcat:gcoriwmhqbbedmaq2btsessnxm
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