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Incremental query evaluation for support vector machines

Danzhou Liu, Kien A. Hua
2009 Proceeding of the 18th ACM conference on Information and knowledge management - CIKM '09  
Support vector machines (SVMs) have been widely used in multimedia retrieval to learn a concept in order to find the best matches.  ...  To address this limitation, we propose an incremental query evaluation technique for these three types of queries.  ...  INTRODUCTION Support Vector Machines (SVMs) have been widely used in various applications.  ... 
doi:10.1145/1645953.1646238 dblp:conf/cikm/LiuH09 fatcat:4nsffswia5ds5bvr552gzzjeyy

Towards Expectation-Maximization by SQL in RDBMS [article]

Kangfei Zhao, Jeffrey Xu Yu, Yu Rong, Ming Liao, Junzhou Huang
2021 arXiv   pre-print
In this paper, we provide an SQL solution that has the potential to support different machine learning modelings.  ...  Integrating machine learning techniques into RDBMSs is an important task since there are many real applications that require modeling (e.g., business intelligence, strategic analysis) as well as querying  ...  The second step is to further find an efficient way to support queries for machine learning in RDBMSs.  ... 
arXiv:2101.09094v1 fatcat:cidssfb2grffzkyddqbldtwmgi

AnalyticDB

Chaoqun Zhan, Fang Zheng, Chengliang Chai, Maomeng Su, Chuangxian Wei, Xiaoqiang Peng, Liang Lin, Sheng Wang, Zhe Chen, Feifei Li, Yue Pan
2019 Proceedings of the VLDB Endowment  
Moreover, these systems are expected to provide high query concurrency and write throughput, and support queries over structured and complex data types (e.g., JSON, vector and texts).  ...  AnalyticDB maintains all-column indexes in an asynchronous manner with acceptable overhead, which provides low latency for complex ad-hoc queries.  ...  Acknowledgement We thank the anonymous reviewers for their insightful comments on this paper.  ... 
doi:10.14778/3352063.3352124 fatcat:u2oa2bbhqbgbfh5iqe5upraf4u

XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning [article]

Sung Whan Yoon, Do-Yeon Kim, Jun Seo, Jaekyun Moon
2020 arXiv   pre-print
We propose XtarNet, which learns to extract task-adaptive representation (TAR) for facilitating incremental few-shot learning.  ...  Learning novel concepts while preserving prior knowledge is a long-standing challenge in machine learning.  ...  Acknowledgements This work was supported by IITP funds from MSIT of Korea (No. 2016-0-00563, No. 2020-0-00626 and No. 2020 for KAIST and AI Graduate School Program at UNIST.  ... 
arXiv:2003.08561v2 fatcat:4eup33cncfdivae37x55wvtk6q

Adaptive learning for relevance feedback: Application to digital mammography

Jung Hun Oh, Yongyi Yang, Issam El Naqa
2010 Medical Physics (Lancaster)  
Methods: In this work, the authors propose a new relevance feedback approach based on incremental learning with support vector machine ͑SVM͒ regression.  ...  In addition, using the same database, the authors achieved a high pathology matching rate greater than 80% between the query and the top retrieved images after relevance feedback.  ...  ACKNOWLEDGMENTS This work was supported in part by NIH Grant Nos. EB009905 and CA128809. a͒  ... 
doi:10.1118/1.3460839 pmid:20879602 pmcid:PMC2927692 fatcat:dqqiqmk5qjdwveuqsvjldxfyoq

The Extreme Value Machine

Ethan M. Rudd, Lalit P. Jain, Walter J. Scheirer, Terrance E. Boult
2018 IEEE Transactions on Pattern Analysis and Machine Intelligence  
learning in the presence of unknown query classes.  ...  While good algorithms that assume inputs from a fixed set of classes exist, e.g., artificial neural networks and kernel machines, it is not immediately obvious how to extend them to perform incremental  ...  V-B we first evaluate its performance in comparison to other classifiers on an open set protocol for the standard machine learning LETTER dataset [12] .  ... 
doi:10.1109/tpami.2017.2707495 pmid:28541894 fatcat:bm3fnvizyzeszeyrhdaxor3xty

Optimization for iterative queries on MapReduce

Makoto Onizuka, Hiroyuki Kato, Soichiro Hidaka, Keisuke Nakano, Zhenjiang Hu
2013 Proceedings of the VLDB Endowment  
We propose OptIQ, a query optimization approach for iterative queries in distributed environment.  ...  Second, OptIQ incrementally evaluates the variant view, so that redundant computations are removed by skipping the evaluation on converged tuples in the variant view.  ...  ) among iterations and incrementally evaluates the iterative query for those tuples.  ... 
doi:10.14778/2732240.2732243 fatcat:r4vbot4hpra4fmmmsyorwvum4u

Vaidurya--a concept-based, context-sensitive search engine for clinical guidelines

Robert Moskovitch, Alon Hessing, Yval Shahar
2004 Studies in Health Technology and Informatics  
Thus, we are developing a digital electronic guideline library (DeGeL) and a set of tools for incremental conversion of free-text guide-lines into increasingly machine-comprehensible representations, which  ...  support automated application.  ...  of evaluating the CPGs relevance to the query and of ranking them.  ... 
pmid:15360791 fatcat:dnbc3hjqh5a4phuze4utl75cza

Beyond Cross-Validation—Accuracy Estimation for Incremental and Active Learning Models

Christian Limberg, Heiko Wersing, Helge Ritter
2020 Machine Learning and Knowledge Extraction  
We evaluate our method with several diverse classifiers and on analytical and real-world benchmark data sets for both incremental and active learning.  ...  For incremental machine-learning applications it is often important to robustly estimate the system accuracy during training, especially if humans perform the supervised teaching.  ...  Support Vector Machine (SVM) A Support Vector Machine (SVM) [37] is a maximum margin classifier, separating classes using a hyperplane defined by a linear combination of so-called support vectors.  ... 
doi:10.3390/make2030018 fatcat:4hepw5mbtzc47nfwh4rzais7ge

The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox [article]

Daniel Crankshaw, Peter Bailis, Joseph E. Gonzalez, Haoyuan Li, Zhao Zhang, Michael J. Franklin, Ali Ghodsi, Michael I. Jordan
2014 arXiv   pre-print
to support training complex models on large datasets.  ...  To provide up-to-date results for these complex models, Velox also facilitates lightweight online model maintenance and selection (i.e., dynamic weighting).  ...  Hellerstein, Tomer Kaftan, Henry Milner, Ion Stoica, Vikram Sreekanti, and the anonymous CIDR reviewers for their thoughtful feedback on this work.  ... 
arXiv:1409.3809v2 fatcat:33a5muyjlbhmrjxn2zkwxlxpxq

Query-Adaptive Ranking with Support Vector Machines for Protein Homology Prediction [chapter]

Yan Fu, Rong Pan, Qiang Yang, Wen Gao
2011 Lecture Notes in Computer Science  
Experiments with the support vector machine (SVM) used as the benchmark learner demonstrate that the proposed algorithm can significantly improve the ranking performance of SVMs in the protein homology  ...  This paper proposes a query-adaptive ranking-function learning algorithm for protein homology prediction.  ...  In this way, incremental learning is also supported.  ... 
doi:10.1007/978-3-642-21260-4_31 fatcat:337sdnuigvewld6ixl4y7xaf4i

Integrating the R Language Runtime System with a Data Stream Warehouse [chapter]

Carlos Ordonez, Theodore Johnson, Simon Urbanek, Vladislav Shkapenyuk, Divesh Srivastava
2017 Lecture Notes in Computer Science  
Our system enables analytic calls in both directions: (1) R calling SQL to evaluate streaming queries; transferring output streaming tables and analyzing them with R operators and functions in the R runtime  ...  Computing mathematical functions or machine learning models on data streams is difficult: a popular approach is to use the R language.  ...  Specifically, we want to develop incremental machine learning algorithms for large SQL tables that can call R mathematical operators and functions.  ... 
doi:10.1007/978-3-319-64471-4_18 fatcat:k5ilbikhove3hchmdytmv6tcjm

Pay-Per-Request Deployment of Neural Network Models Using Serverless Architectures

Zhucheng Tu, Mengping Li, Jimmy Lin
2018 Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations  
We demonstrate the serverless deployment of neural networks for model inferencing in NLP applications using Amazon's Lambda service for feedforward evaluation and DynamoDB for storing word embeddings.  ...  All virtual machine management is handled behind the scenes by the cloud provider without any direct developer intervention.  ...  This research was supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada.  ... 
doi:10.18653/v1/n18-5002 dblp:conf/naacl/TuLL18 fatcat:ipm2zinp2nf6bh3n4lykzz7ezy

Few-shot Learning with LSSVM Base Learner and Transductive Modules [article]

Haoqing Wang, Zhi-Hong Deng
2020 arXiv   pre-print
introduce multi-class least squares support vector machine as our base learner which obtains better generation than existing ones with less computational overhead; 2) further, in order to utilize the  ...  mechanism and adding the prototypes of the query set with pseudo labels to the support set as the pseudo support samples.  ...  In this work, we use multi-class least squares support vector machine as the base learner which further improves accuracy and reduces computational overhead.  ... 
arXiv:2009.05786v1 fatcat:nil5ysugpvdl7oattxgkuu7bqy

Understanding Static Code Warnings: an Incremental AI Approach [article]

Xueqi Yang, Zhe Yu, Junjie Wang, Tim Menzies
2020 arXiv   pre-print
Using an incremental support vector machine mechanism, this AI tool can quickly learn to distinguish spurious false alarms from more serious matters that deserve further attention.  ...  Hence, an important issue for such systems is how to acquire the knowledge needed for their inference. This paper assesses active learning methods for acquiring knowledge for "static code warnings".  ...  Exception for support vector machines, we followed the advice of a previous publication (Krishna et al., 2016) which suggested using a linear, and a not radial, kernel). Support Vector Machine.  ... 
arXiv:1911.01387v3 fatcat:jhlbjcln7jej7bqiuwiry4ribq
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