[1] |
IBM What Is Big Data: Bring Big Data to the Enterprise. 2012. [online] Available at: http://www-01.ibm.com/software/data/bigdata/. |
[2] |
HilbertM, LópezP. The world’s technological capacity to store, communicate, and compute information. Science2011, 332:60-65. |
[3] |
IDC. 2014. Available at: https://www.emc.com/leadership/digital-universe/2014iview/executive-summary.htm. (Accesses March 2017) |
[4] |
SettlesB. Active learning. Synth Lect Artif Intell Mach Learn2012, 6:1-114. · Zbl 1270.68006 |
[5] |
TomanekK, OlssonF. A web survey on the use of active learning to support annotation of text data. In: Proceedings of the NAACL HLT 2009 Workshop on Active Learning for Natural Language Processing, pp. 45-48. Association for Computational Linguistics, 2009. |
[6] |
HesabiZR, TariZ, GoscinskiA, FahadA, KhalilI, QueirozC. Data summarization techniques for big data—a survey. In: Handbook on Data Centers. New York: Springer; 2015, 1109-1152. |
[7] |
VitterJS. Random sampling with a reservoir. ACM Trans Math Softw1985, 11:37-57. · Zbl 0562.68028 |
[8] |
MichalskiRS. On the selection of representative samples from large relational tables for inductive inference. University of Illinois (Chicago circle) Tech. Report, 1975. |
[9] |
WaldA. On the efficient design of statistical investigations. Ann Math Stat1943, 14:134-140. · Zbl 0060.30109 |
[10] |
LiuH (ed.), MotodaH (ed.), eds. Instance Selection and Construction for Data Mining, vol. 608. US: Springer Science & Business Media; 2013. |
[11] |
AntalE, TilléY. A direct bootstrap method for complex sampling designs from a finite population. J Am Stat Assoc2011, 106:534-543. · Zbl 1232.62030 |
[12] |
AfshartousD. Sample size determination for binomial proportion confidence intervals: an alternative perspective motivated by a legal case. Am Stat2008, 62:27-31. |
[13] |
O’NeillB. Some useful moment results in sampling problems. Am Stat2014, 68:282-296. · Zbl 07653670 |
[14] |
ZhangL. Sample mean and sample variance: their covariance and their (in) dependence. Am Stat2007, 61:159-160. |
[15] |
GregoireTG, AffleckDLR. Estimating desired sample size for simple random sampling of a skewed population. Am Stat. In press. · Zbl 07663938 |
[16] |
FedorovVV. Theory of Optimal Experiments. Philadelphia, PA: Elsevier; 1972. |
[17] |
CochranWG. Sampling Techniques. 3rd ed.New York: John Wiley & Sons; 1977. · Zbl 0353.62011 |
[18] |
HedayatAS, Kumar SinhaB. Design and Inference Infinite Population Sampling. New York: Wiley; 1991. · Zbl 0850.62160 |
[19] |
GuB, HuF, LiuH. Sampling and its application in data mining: a survey. Singapore: National University of Singapore; 2000. |
[20] |
HannekeS. Theory of disagreement‐based active learning. Found Trends Mach Learn2014, 7:131-309. · Zbl 1327.68193 |
[21] |
ZhuX, LaffertyJ, GhahramaniZ. Combining active learning and semi‐supervised learning using gaussian fields and harmonic functions. In: Proceedings of the ICML Workshop on the Continuum from Labeled to Unlabeled Data, pp. 58-65, 2003. |
[22] |
ZhangJ, XuJ, LiaoS. Sampling methods for summarizing unordered vehicle‐tovehicle data streams. Transportation Research Part C—Emerging Technologies2012, 23:56-67. |
[23] |
DashM, NgW. Efficient reservoir sampling for transactional data streams. In: Proceedings of IEEE International Conference on Data Mining (ICDM), pp. 662-666, 2006. |
[24] |
AggarwalCC. On biased reservoir sampling in the presence of stream evolution. In: Proceedings of the 32nd International Conference on Very large Data Bases (VLDB), pp. 607-618, 2006. |
[25] |
GhoshD, VogtA. A modification of Poisson sampling. In: Proceedings of the American Statistical Association, Survey Research Methods Section, pp. 198-199, 1999. |
[26] |
BabcockB, DatarM, MotwaniR. Sampling from a moving window over streaming data. In: Proceedings of the 13th Annual ACM‐SIAM Symposium on Discrete Algorithms (SODA). Society for Industrial and Applied Mathematics, Philadelphia, pp. 633-634, 2002. · Zbl 1093.68571 |
[27] |
Hua‐HuiC, LiaoK‐L. Weighted random sampling based hierarchical amnesic synopses for data streams. In: 2010 5th International Conference on Computer Science and Education (ICCSE), pp. 1816-1820, IEEE, 2010. |
[28] |
AcharyaS, PoosalaV, RamaswamyS. Selectivity estimation in spatial databases. In: Proceedings of SIGMOD, June 1999. |
[29] |
Al‐KatebM, LeeBS. Adaptive stratified reservoir sampling over heterogeneous data streams. Inf Syst2014, 39:199-216. |
[30] |
LiuT, WangF, AgrawalG. Stratified sampling for data mining on the deep web. Front Comp Sci2012, 6:179-196. · Zbl 1251.68182 |
[31] |
KurantM, GjokaM, ButtsCT, MarkopoulouA. Walking on a graph with a magnifying glass: stratified sampling via weighted random walks. In: Proceedings of the ACM SIGMETRICS Joint International Conference on Measurement and Modeling of Computer Systems, pp. 281-292. ACM, 2011. |
[32] |
YeY, WuQ, Zhexue HuangJ, NgMK, LiX. Stratified sampling for feature subspace selection in random forests for high dimensional data. Pattern Recogn2013, 46:769-787. |
[33] |
HollandPW. Statistics and causal inference. J Am Stat Assoc1986, 81:945-960. · Zbl 0607.62001 |
[34] |
AlemiF, ElRafeyA, AvramovicI. Covariate balancing through naturally occurring strata. Health Serv Res2016. https://doi.org/10.1111/1475-6773.12628 · doi:10.1111/1475-6773.12628 |
[35] |
MiratrixLW, SekhonJS, BinY. Adjusting treatment effect estimates by post‐stratification in randomized experiments. J R Stat Soc Series B Stat Methodology2013, 75:369-396. · Zbl 07555452 |
[36] |
NeymanJ. Contribution to the theory of sampling human populations. J Am Stat Assoc1938, 33:101-116. · Zbl 0018.22603 |
[37] |
BreslowNE, HolubkovR. Maximum Likelihood Estimation of Logistic Regression Parameters under Two‐phase, Outcome‐dependent Sampling. J R Stat Soc Ser B Stat Methodol1997, 59:447-461. · Zbl 0886.62071 |
[38] |
ChatterjeeN, ChenY‐H. Maximum likelihood inference on a mixed conditionally and marginally specified regression model for genetic epidemiologic studies with two‐phase sampling. J R Stat Soc Ser B Stat Methodol2007, 69:123-142. · Zbl 1120.62096 |
[39] |
BreslowNE, WellnerJA. Weighted likelihood for semiparametric models and two‐phase stratified samples, with application to cox regression. Scand J Stat2007, 34:86-102. · Zbl 1142.62014 |
[40] |
YamaneT. Elementary Sampling Theory. Englewood Cliffs, NJ: Prentice Hall; 1967. · Zbl 0147.38002 |
[41] |
NguyenTT, SongI. Centrality clustering‐based sampling for big data visualization. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 1911-1917. IEEE, 2016. |
[42] |
SharmaS, KhanMGM. Determining optimum cluster size and sampling unit for multivariate study. In: 2015 2nd Asia‐Pacific World Congress on Computer Science and Engineering (APWC on CSE), pp. 1-4. IEEE, 2015. |
[43] |
InoueT, KrishnaA, GopalanRP. Multidimensional cluster sampling view on large databases for approximate query processing. In: 2015 I.E. 19th International Enterprise Distributed Object Computing Conference (EDOC), pp. 104-111. IEEE, 2015. |
[44] |
ThompsonSK. Adaptive cluster sampling. J Am Stat Assoc1990, 85:1050-1059. · Zbl 1330.62070 |
[45] |
FieldCA, WelshAH. Bootstrapping clustered data. J R Stat Soc B2007, 69:369-390. · Zbl 07555357 |
[46] |
FieldCA, PangZ, WelshAH. Bootstrapping data with multiple levels of variation. Can J Stat2008, 36:521-539. · Zbl 1166.62026 |
[47] |
SamantaM, WelshAH. Bootstrapping for highly unbalanced clustered data. Comput Stat Data Anal2013, 59:70-81. · Zbl 1400.62134 |
[48] |
ChatterjeeS, BoseA. Generalized bootstrap for estimating equations. Ann Stat2005, 33:414-436. · Zbl 1065.62073 |
[49] |
Salibián‐BarreraM, Van AelstS, WillemsG. Principal components analysis based on multivariate MM estimators with fast and robust bootstrap. J Am Stat Assoc2006, 101:1198-1211. · Zbl 1120.62319 |
[50] |
MacKinnonJG, WebbMD. Wild bootstrap inference for wildly different cluster sizes. J Appl Econ2016, 32:233-254. |
[51] |
ParentePMDC, SilvaS. Quantile regression with clustered data. J Econ Methods2016, 5:1-15. · Zbl 1345.62182 |
[52] |
PalmerCR, FaloutsosC. Density biased sampling: an improved method for data mining and clustering. ACM2000, 29:82-92. |
[53] |
PoosalaV, IoannidisY. Selectivity estimation without the attribute value independence assumption. In: Proceedings of Very Large Data Bases Conference, pp. 486-495, 1997. |
[54] |
ChaudhuriS, MotwaniR, NarasayyaV. On random sampling over joins. In: Proceedings of SIGMOD, pp. 263-274, June 1999. |
[55] |
KornF, JohnsonT, JagadishH. Range selectivity estimation for continuous attribute. In: Proceedings of 11th Intl Conf. SSDBMs, 1999. |
[56] |
VitterJS, WangM, IyerBR. Data cube approximation and histograms via wavelets. In: Proceedings of 1998 ACM CIKM International Conference on Information and Knowledge Management, 1998. |
[57] |
MatiasY, VitterJS, WangM. Wavelet‐based histograms for selectivity estimation. In: Proceedings of 1998 ACM SIGMOD International Conference on Management of Data, 1998. |
[58] |
LeeJ, KimD, ChungC. Multi‐dimensional selectivity estimation using compressed histogram information. In: Proceedings of 1999ACM SIGMOD International Conference on Management of Data, 1999. |
[59] |
BlohsfeldB, KorusD, SeegerB. A comparison of selectivity estimators for range queries on metric attributes. Proceedings of 1999 ACM SIGMOD International Conference on Management of Data, 1999. |
[60] |
ScottD. Multivariate Density Estimation: Theory, Practice and Visualization. Hoboken, NJ: John Wiley & Sons; 1992. · Zbl 0850.62006 |
[61] |
SilvermanBW. Density estimation for statistics and data analysis. In: Monographs on Statistics and Applied Probability. Boca Raton, FL: Chapman & Hall; 1986. · Zbl 0617.62042 |
[62] |
KolliosG, GunopulosD, KoudasN, BerchtoldS. Efficient biased sampling for approximate clustering and outlier detection in large data sets. IEEE Trans Knowl Data Eng2003, 15:1170-1187. |
[63] |
IversenTF, EllekildeL‐P. Kernel density estimation based self‐learning sampling strategy for motion planning of repetitive tasks. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1380-1387. IEEE, 2016. |
[64] |
PejoskiS, KafedziskiV. Wavelet image decomposition based variable density compressive sampling in mri. In: Telecommunications Forum (TELFOR), 2011 19th, pp. 635-638. IEEE, 2011. |
[65] |
LewisD, CatlettJ. Heterogeneous uncertainty sampling for supervised learning. In: Proceedings of the International Conference on Machine Learning (ICML), pp. 148-156. Morgan Kaufmann, 1994. |
[66] |
SharmaM, BilgicM. Evidence‐based uncertainty sampling for active learning. Data Mining Knowl Discov2017, 31:164-202. · Zbl 1411.68121 |
[67] |
BilgicM, MihalkovaL, GetoorL. Active learning for networked data. In: Proceedings of the 27th International Conference on Machine Learning, pp. 79-86, 2010. |
[68] |
ChaoC, CakmakM, ThomazAL. Transparent active learning for robots. In: 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI), IEEE, pp. 317-324, 2010. |
[69] |
StanitsasP, CherianA, MorellasV, PapanikolopoulosN. Active constrained clustering via non‐iterative uncertainty sampling. In: IROS, 2016, pp. 4027-4033. |
[70] |
PrudêncioRBC, SoaresC, Bernarda LudermirT. Uncertainty sampling‐based active selection of datasetoids for meta‐learning. in: ICANN (2), pp. 454-461, 2011. |
[71] |
BhattN, ThakkarA, GanatraA, BhattN. The multi‐criteria ranking approach to classification algorithms using uncertainty sampling method of active meta learning; 2014. |
[72] |
MinakawaM, RaytchevB, TamakiT, KanedaK. Image sequence recognition with active learning using uncertainty sampling. In: The 2013 International Joint Conference on Neural Networks (IJCNN), pp. 1-6. IEEE, 2013. |
[73] |
LughoferE, PratamaM. On‐line active learning in data stream regression using uncertainty sampling based on evolving generalized fuzzy models. In: IEEE Transactions on Fuzzy Systems, 2017. |
[74] |
NguforC, WojtusiakJ. Learning from large distributed data: a scaling down sampling scheme for efficient data processing. Int J Mach Learn Comput2014, 4:216-224. |
[75] |
ZhangT, OlesF. A probability analysis on the value of unlabeled data for classification problems. In: Proceedings of the International Conference on Machine Learning, 2000. |
[76] |
BrinkerK. Incorporating diversity in active learning with support vector machines. In: ICML, 2003. |
[77] |
HoiSCH, JinR, ZhuJ, LyuMR. Batch mode active learning and its application to medical image classification. In: ICML, 2006. |
[78] |
AzimiJ, FernA, Zhang‐FernX, BorradaileG, HeeringaB. Batch active learning via coordinated matching. arXiv preprint arXiv:1206.6458, 2012. |
[79] |
WangZ, YeJ. Querying discriminative and representative samples for batch mode active learning. ACM Trans Knowl Discov Data2015, 9:17. |
[80] |
WeiK, IyerRK, BilmesJA. Submodularity in data subset selection and active learning. In: ICML, pp. 1954-1963, 2015. |
[81] |
Chattopadhyay, R, FanW, DavidsonI, PanchanathanS, YeJ. Joint transfer and batch‐mode active learning. In: ICML 3, pp. 253-261, 2013. |
[82] |
MitchellT. Generalization as search. Artificial Intell1982, 18:203-226. https://doi.org/10.1016/0004-3702(82)90040-6. · doi:10.1016/0004-3702(82)90040-6 |
[83] |
DasguptaS. Two faces of active learning. Theor Comput Sci2011, 412:1767-1781. · Zbl 1209.68408 |
[84] |
HannekeS. Theory of active learning. Version 1.1, 2014. Available at: http://www.stevehanneke.com. |
[85] |
CohnD, AtlasL, LadnerR. Improving generalization with active learning. Mach Learn1994, 15:201-221. https://doi.org/10.1007/BF00993277. · doi:10.1007/BF00993277 |
[86] |
SeungHS, OpperM, SompolinskyH. Query by committee. In: Proceedings of the ACM Workshop on Computational Learning Theory, pp. 287-294. ACM, 1992. 10.1145/130385.130417 |
[87] |
FreundY, SeungHS, ShamirE, TishbyN. Selective samping using the query by committee algorithm. Mach Learn1997, 28:133-168. https://doi.org/10.1023/A:1007330508534. · Zbl 0881.68093 · doi:10.1023/A:1007330508534 |
[88] |
OlssonF. A literature survey of active machine learning in the context of natural language processing; 2009. |
[89] |
BreimanL. Bagging predictors. Mach Learn1996, 24:123-140. · Zbl 0858.68080 |
[90] |
FreundY, SchapireRE. A decision‐theoretic generalization of on‐line learning and application to boosting. J Comput Syst Sci1997, 55:119-139. · Zbl 0880.68103 |
[91] |
MelvilleP, MooneyRJ. Diverse ensembles for active learning. In: Proceedings of the 21st International Conference on Machine Learning (ICML‐2004), pp. 584-591. Banff, Canada, 2004. |
[92] |
StefanowskiJ, PachockiM. Comparing performance of committee based approaches to active learning. In: Recent Advances in Intelligent Information Systems. Warszawa: Wydawnictwo EXIT; 2009, 457-470. |
[93] |
DzeroskiS, ZenkoB. Is combining classifiers with stacking better than selecting the best one?Mach Learn2004, 54:255-273. · Zbl 1101.68077 |
[94] |
CaruanaR, MunsonA, Niculescu‐MizilA. Getting the most out of ensemble selection. In: Proceedings of International Conference on Data Mining (ICDM), pp. 828-833, 2006. |
[95] |
LuZ, WuX, BongardJC. Active learning through adaptive heterogeneous ensembling. IEEE Trans Knowl Data Eng2015, 27:368-381. |
[96] |
BalcanM‐F, BeygelzimerA, LangfordJ. Agnostic active learning. J Comput Syst Sci2009, 75:78-89. · Zbl 1162.68516 |
[97] |
HannekeS. A bound on the label complexity of agnostic active learning. In: Proceedings of the 24th International Conference on Machine Learning, 2007. |
[98] |
DasguptaS, HsuD, MonteleoniC. A general agnostic active learning algorithm. In: Advances in Neural Information Processing Systems 20, 2007. |
[99] |
BalcanM‐F, BroderA, ZhangT. Margin based active learning. In: Proceedings of the 20th Conference on Learning Theory, 2007. · Zbl 1203.68136 |
[100] |
BeygelzimerA, DasguptaS, LangfordJ. Importance weighted active learning. In: Proceedings of the 26th International Conference on Machine Learning, 2009. |
[101] |
FriedmanE. Active learning for smooth problems. In: Proceedings of the 22nd Conference on Learning Theory, 2009. |
[102] |
BalcanM‐F, HannekeS, VaughanJW. The true sample complexity of active learning. Mach Learn2010, 80:111-139. · Zbl 1470.68078 |
[103] |
HannekeS. Rates of convergence in active learning. Ann Stat2011, 39:333-361. · Zbl 1274.62510 |
[104] |
KoltchinskiiV. Rademacher complexities and bounding the excess risk in active learning. J Mach Learn Res2010, 11:2457-2485. · Zbl 1242.62088 |
[105] |
BeygelzimerA, HsuD, LangfordJ, ZhangT. Agnostic active learning without constraints. In: Advances in Neural Information Processing Systems 23, 2010. |
[106] |
HsuD. Algorithms for active learning. PhD Thesis, Department of Computer Science and Engineering,School of Engineering, University of California, San Diego, 2010. |
[107] |
HannekeS. Activized learning: transforming passive to active with improved label complexity. J Mach Learn Res2012, 13:1469-1587. · Zbl 1303.68103 |
[108] |
El‐YanivR, WienerY. Active learning via perfect selective classification. J Mach Learn Res2012, 13:255-279. · Zbl 1283.68287 |
[109] |
HannekeS, YangL. Surrogate losses in passive and active learning. arXiv:1207.3772, 2012. |
[110] |
HannekeS. Teaching dimension and the complexity of active learning. In: Proceedings of the 20th Conference on Learning Theory, 2007. · Zbl 1203.68151 |
[111] |
El‐YanivR, WienerY. On the foundations of noise‐free selective classification. J Mach Learn Res2010, 11:1605-1641. · Zbl 1242.68218 |
[112] |
WienerY. Theoretical foundations of selective prediction. PhD Thesis, The Technion — Israel Institute of Technology, 2013. |
[113] |
KornerC, WrobelS. Multi‐class ensemble‐based active learning. In: Proceedings of The 17th European Conference on Machine Learning and the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, pp. 687-694. Berlin: Springer‐Verlag, 2006. |
[114] |
LinJ. Divergence measures based on the Shannon entropy. IEEE Trans Inf Theory1991, 37:145-151. · Zbl 0712.94004 |
[115] |
PereiraFCN, TishbyN, LeeL. Distributional clustering of English words. In: Proceedings of the 31st Annual Meeting of the Association for Computational Linguistics, pp. 183-190. Columbus, OH: ACL, 1993. |
[116] |
KullbackS, LeiblerRA. On information and sufficiency. Ann Math Stat1951, 22:79-86. · Zbl 0042.38403 |
[117] |
EngelsonSP, DaganI. 1996. Minimizing manual annotation cost in supervised training from corpora. In: Proceedings of the 34th Annual Meeting of the Association for Computational Linguistics, pp. 319-326. Santa Cruz, CA: ACL. |
[118] |
NgaiG, YarowskyD. Rule writing or annotation: Costefficient resource usage for base noun phrase chunking. In: Proceedings of the 38th Annual Meeting on Association for Computational Linguistics, pp. 117-125. Hong‐Kong: ACL, 2000. |
[119] |
ChalonerK, VerdinelliI. Bayesian experimental design: a review. Stat Sci1995, 10:273-304. · Zbl 0955.62617 |
[120] |
SmithK. On the standard deviations of adjusted and interpolated values of an observed polynomial function and its constants and the guidance they give towards a proper choice of the distribution of observations. Biometrika1918, 12:1-85. |
[121] |
FedorovV. Optimal experimental design. WIREs Comput Stat2010, 2:581-589. |
[122] |
DetteH, StuddenWJ. Geometry of E‐optimality. Ann Stat1993, 21:416-433. · Zbl 0780.62057 |
[123] |
ElfvingG. Optimum allocation in linear regression theory. Ann Math Stat1952, 23:255-262. · Zbl 0047.13403 |
[124] |
SacksJ, YlvisakerD. Designs for regression problems with correlated errors. Ann Math Stat1966, 37:66-89. · Zbl 0152.17503 |
[125] |
MacKayDJC. Information‐based objective functions for active data selection. Neural Comput1992, 4:590-604. |
[126] |
ScheinAI, UngarLH. Active learning for logistic regression: an evaluation. Mach Learn2007, 68:235-265. · Zbl 1470.68170 |
[127] |
HoiSCH, JinR, LyuMR. Large‐scale text categorization by batch mode active learning. In: Proceedings of the International Conference on theWorldWideWeb, pp. 633-642. ACM, 2006. doi: 10.1145/1135777.1135870 |
[128] |
Ramirez‐LoaizaME, SharmaM, KumarG, BilgicM. Active learning: an empirical study of common baselines. Data Mining Knowl Discov2016, 31:287-313. |
[129] |
RoyN, McCallumA. Toward optimal active learning through sampling estimation of error reduction. In: Proceedings of the International Conference on Machine Learning (ICML), pp. 441-448. Morgan Kaufmann; 2001. |
[130] |
dos SantosDP, de CarvalhoACPLF. Comparison of active learning strategies and proposal of a multiclass hypothesis space search. In: International Conference on Hybrid Artificial Intelligence Systems, pp. 618-629. Springer International Publishing, 2014. |
[131] |
SettlesB, CravenM. An analysis of active learning strategies for sequence labeling tasks. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1070-1079; 2008. |
[132] |
ZhuJ, WangH, TsouBK, MaM. Active learning with sampling by uncertainty and density for data annotations. IEEE Trans Audio Speech Lang Process2010, 18:1323-1331. |
[133] |
IencoD, ZliobaiteI, PfahringerB. High density‐focused uncertainty sampling for active learning over evolving stream data. In: BigMine, pp. 133-148, 2014. |
[134] |
FuY, ZhuX, LiB. A survey on instance selection for active learning. Knowl Inf Syst2013, 35:1-35. |
[135] |
BouneffoufD. Exponentiated gradient exploration for active learning. C R Geosci2016, 5:1. |
[136] |
LuoC, JiY, DaiX, ChenJ. Active learning with transfer learning. In: Proceedings of ACL 2012 Student Research Workshop, pp. 13-18. Association for Computational Linguistics, 2012. |
[137] |
ShaoH, TaoF, RuiX. Transfer active learning by querying committee. J Zhejiang Univ Sci C2014, 15:107-118. |
[138] |
HannekeS, YangL. Minimax analysis of active learning. J Mach Learn Res2015, 16:3487-3602. · Zbl 1351.68210 |
[139] |
ProvostF, JensenD, OatesT. Efficient progressive sampling. In: Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining, pp. 23-32, 1999. |
[140] |
MeekC, TheissonB, HeckerrnanD. The learning‐curve sampling method applied to model‐ based clustering. J Mach Learn Res2002, 2:397-418. · Zbl 1007.68082 |
[141] |
JohnGH, LangleyP. Static versus dynamic sampling for data mining. In: KDD, 96, pp. 367-370, 1996. |
[142] |
SatyanarayanaA. Intelligent sampling for big data using bootstrap sampling and chebyshev inequality. In: 2014 I.E. 27th Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1-6. IEEE, 2014. |