-
1
-
-
68249088215
-
Model selection for the LS-SVM. Application to handwriting recognition
-
Adankon MM, Cheriet M (2009) Model selection for the LS-SVM. Application to handwriting recognition. Pattern Recognit 42(12):3264–3270
-
(2009)
Pattern Recognit
, vol.42
, Issue.12
, pp. 3264-3270
-
-
Adankon, M.M.1
Cheriet, M.2
-
2
-
-
84908212433
-
Active learning with model selection. In: Proceedings of AAAI’14
-
Ali A, Caruana R, Kapoor A (2014) Active learning with model selection. In: Proceedings of AAAI’14, pp 1673–1679
-
(2014)
pp 1673–1679
-
-
Ali, A.1
Caruana, R.2
Kapoor, A.3
-
5
-
-
0034241361
-
Gradient-based optimization of hyperparameters
-
Bengio Y (2000) Gradient-based optimization of hyperparameters. Neural Comput 12(8):1889–1900
-
(2000)
Neural Comput
, vol.12
, Issue.8
, pp. 1889-1900
-
-
Bengio, Y.1
-
6
-
-
84857855190
-
Random search for hyper-parameter optimization
-
Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. J Mach Learn Res 13:281–305
-
(2012)
J Mach Learn Res
, vol.13
, pp. 281-305
-
-
Bergstra, J.1
Bengio, Y.2
-
8
-
-
84938384905
-
Hyperopt: a Python library for optimizing the hyperparameters of machine learning algorithms
-
Bergstra J, Yamins D, Cox DD (2013) Hyperopt: a Python library for optimizing the hyperparameters of machine learning algorithms. In: Proceedings of SciPy 2013, pp 13–20
-
(2013)
Proceedings of SciPy
, vol.2013
, pp. 13-20
-
-
Bergstra, J.1
Yamins, D.2
Cox, D.D.3
-
11
-
-
0037361994
-
Ranking learning algorithms: using IBL and meta-learning on accuracy and time results
-
Brazdil P, Soares C, da Costa JP (2003) Ranking learning algorithms: using IBL and meta-learning on accuracy and time results. Mach Learn 50(3):251–277
-
(2003)
Mach Learn
, vol.50
, Issue.3
, pp. 251-277
-
-
Brazdil, P.1
Soares, C.2
da Costa, J.P.3
-
14
-
-
85071319367
-
Bigtable: a distributed storage system for structured data. In: Proceedings of OSDI’06
-
Chang F, Dean J, Ghemawat S, Hsieh WC, Wallach DA, Burrows M et al. (2006) Bigtable: a distributed storage system for structured data. In: Proceedings of OSDI’06, pp 205–218
-
(2006)
pp 205–218
-
-
Chang, F.1
Dean, J.2
Ghemawat, S.3
Hsieh, W.C.4
Wallach, D.A.5
Burrows, M.6
-
19
-
-
85030321143
-
MapReduce: simplified data processing on large clusters. In: Proceedings of OSDI’04
-
Dean J, Ghemawat S (2004) MapReduce: simplified data processing on large clusters. In: Proceedings of OSDI’04, pp 137–150
-
(2004)
pp 137–150
-
-
Dean, J.1
Ghemawat, S.2
-
20
-
-
84949921865
-
Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves. In: Proceedings of IJCAI’15
-
Domhan T, Springenberg JT, Hutter F (2015) Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves. In: Proceedings of IJCAI’15, pp 3460–3468
-
(2015)
pp 3460–3468
-
-
Domhan, T.1
Springenberg, J.T.2
Hutter, F.3
-
21
-
-
0035376039
-
Case study: a data warehouse for an academic medical center
-
Einbinder JS, Scully KW, Pates RD, Schubart JR, Reynolds RE (2001) Case study: a data warehouse for an academic medical center. J Healthc Inf Manag. 15(2):165–175
-
(2001)
J Healthc Inf Manag.
, vol.15
, Issue.2
, pp. 165-175
-
-
Einbinder, J.S.1
Scully, K.W.2
Pates, R.D.3
Schubart, J.R.4
Reynolds, R.E.5
-
22
-
-
84965128050
-
Efficient and robust automated machine learning. In: Proceedings of NIPS’15
-
Feurer M, Klein A, Eggensperger K, Springenberg J, Blum M, Hutter F (2015a) Efficient and robust automated machine learning. In: Proceedings of NIPS’15, pp 2944–2952
-
(2015)
pp 2944–2952
-
-
Feurer, M.1
Klein, A.2
Eggensperger, K.3
Springenberg, J.4
Blum, M.5
Hutter, F.6
-
23
-
-
84959891641
-
Initializing Bayesian hyperparameter optimization via meta-learning. In: Proceedings of AAAI’15
-
Feurer M, Springenberg T, Hutter F (2015b) Initializing Bayesian hyperparameter optimization via meta-learning. In: Proceedings of AAAI’15, pp 1128–1135
-
(2015)
pp 1128–1135
-
-
Feurer, M.1
Springenberg, T.2
Hutter, F.3
-
24
-
-
0013105971
-
-
Proceedings ECML/PKDD Workshop on Integrating Aspects of Data Mining, Decision Support and Meta-Learning
-
Fürnkranz J, Petrak J (2001) An evaluation of landmarking variants. In: Proceedings ECML/PKDD Workshop on Integrating Aspects of Data Mining, Decision Support and Meta-Learning 2001, pp 57–68
-
(2001)
Petrak J (2001) An evaluation of landmarking variants. In
, pp. 57-68
-
-
Fürnkranz, J.1
-
25
-
-
85053970271
-
-
Chapman and Hall/CRC, Boca Raton
-
Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, Rubin DB (2013) Bayesian data analysis, 3rd edn. Chapman and Hall/CRC, Boca Raton
-
(2013)
Bayesian data analysis
-
-
Gelman, A.1
Carlin, J.B.2
Stern, H.S.3
Dunson, D.B.4
Vehtari, A.5
Rubin, D.B.6
-
26
-
-
85049142979
-
-
Google Prediction API homepage (2016). Accessed 20 January 2016
-
Google Prediction API homepage (2016) https://cloud.google.com/prediction/docs. Accessed 20 January 2016
-
-
-
-
27
-
-
33749140655
-
Efficiently determining the starting sample size for progressive sampling. In: Proceedings of ECML’01
-
Gu B, Liu B, Hu F, Liu H (2001) Efficiently determining the starting sample size for progressive sampling. In: Proceedings of ECML’01, pp 192–202
-
(2001)
pp 192–202
-
-
Gu, B.1
Liu, B.2
Hu, F.3
Liu, H.4
-
28
-
-
56549111881
-
A novel LS-SVMs hyper-parameter selection based on particle swarm optimization
-
Guo XC, Yang JH, Wu CG, Wang CY, Liang YC (2008) A novel LS-SVMs hyper-parameter selection based on particle swarm optimization. Neurocomputing 71(16–18):3211–3215
-
(2008)
Neurocomputing
, vol.71
, Issue.16-18
, pp. 3211-3215
-
-
Guo, X.C.1
Yang, J.H.2
Wu, C.G.3
Wang, C.Y.4
Liang, Y.C.5
-
29
-
-
84951047310
-
ChaLearn AutoML challenge. In: Proceedings of IJCNN’15
-
Guyon I, Bennett K, Cawley GC, Escalante HJ, Escalera S, Ho TK, Macià N, Ray B, Saeed M, Statnikov AR, Viegas E (2015) Design of the 2015 ChaLearn AutoML challenge. In: Proceedings of IJCNN’15, pp 1–8
-
(2015)
pp 1–8
-
-
Guyon, I.1
Bennett, K.2
Cawley, G.C.3
Escalante, H.J.4
Escalera, S.5
Ho, T.K.6
Macià, N.7
Ray, B.8
Saeed, M.9
Statnikov, A.R.10
-
31
-
-
84955516630
-
On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning. In: Proceedings of AISTATS’14
-
Hoffman MD, Shahriari B, de Freitas N (2014) On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning. In: Proceedings of AISTATS’14, pp 365–374
-
(2014)
pp 365–374
-
-
Hoffman, M.D.1
Shahriari, B.2
de Freitas, N.3
-
33
-
-
84868554032
-
Sequential model-based optimization for general algorithm configuration. In: Proceedings of LION’11
-
Hutter F, Hoos HH, Leyton-Brown K (2011) Sequential model-based optimization for general algorithm configuration. In: Proceedings of LION’11, pp 507–523
-
(2011)
pp 507–523
-
-
Hutter, F.1
Hoos, H.H.2
Leyton-Brown, K.3
-
34
-
-
85049144997
-
An efficient approach for assessing hyperparameter importance. In: Proceedings of ICML’14
-
Hutter F, Hoos H, Leyton-Brown K (2014) An efficient approach for assessing hyperparameter importance. In: Proceedings of ICML’14, pp 754–762
-
(2014)
pp 754–762
-
-
Hutter, F.1
Hoos, H.2
Leyton-Brown, K.3
-
35
-
-
85049139321
-
Static versus dynamic sampling for data mining. In: Proceedings of KDD’96
-
John GH, Langley P (1996) Static versus dynamic sampling for data mining. In: Proceedings of KDD’96, pp 367–370
-
(1996)
pp 367–370
-
-
John, G.H.1
Langley, P.2
-
36
-
-
84906902062
-
An overview of free software tools for general data mining. In: Proceedings of MIPRO’14
-
Jovic A, Brkic K, Bogunovic N (2014) An overview of free software tools for general data mining. In: Proceedings of MIPRO’14, pp 1112–1117
-
(2014)
pp 1112–1117
-
-
Jovic, A.1
Brkic, K.2
Bogunovic, N.3
-
37
-
-
2142781609
-
Methods and criteria for model selection
-
Kadane JB, Lazar NA (2004) Methods and criteria for model selection. J Am Stat Assoc 99(465):279–290
-
(2004)
J Am Stat Assoc
, vol.99
, Issue.465
, pp. 279-290
-
-
Kadane, J.B.1
Lazar, N.A.2
-
38
-
-
84908279482
-
Hyperopt-sklearn: automatic hyperparameter configuration for scikit-learn
-
Komer B, Bergstra J, Eliasmith C (2014) Hyperopt-sklearn: automatic hyperparameter configuration for scikit-learn. In: Proceedings of SciPy 2014, pp 33–39
-
(2014)
Proceedings of SciPy
, vol.2014
, pp. 33-39
-
-
Komer, B.1
Bergstra, J.2
Eliasmith, C.3
-
39
-
-
85084017339
-
MLbase: a distributed machine-learning system
-
Kraska T, Talwalkar A, Duchi JC, Griffith R, Franklin MJ, Jordan MI (2013) MLbase: a distributed machine-learning system. In: Proceedings of CIDR’13
-
(2013)
In: Proceedings of CIDR’13
-
-
Kraska, T.1
Talwalkar, A.2
Duchi, J.C.3
Griffith, R.4
Franklin, M.J.5
Jordan, M.I.6
-
42
-
-
31844442667
-
Predicting relative performance of classifiers from samples. In: Proceedings of ICML’05
-
Leite R, Brazdil P (2005) Predicting relative performance of classifiers from samples. In: Proceedings of ICML’05, pp 497–503
-
(2005)
pp 497–503
-
-
Leite, R.1
Brazdil, P.2
-
43
-
-
77956031602
-
Active testing strategy to predict the best classification algorithm via sampling and metalearning. In: Proceedings of ECAI’10
-
Leite R, Brazdil P (2010) Active testing strategy to predict the best classification algorithm via sampling and metalearning. In: Proceedings of ECAI’10, pp 309–314
-
(2010)
pp 309–314
-
-
Leite, R.1
Brazdil, P.2
-
44
-
-
84864928603
-
Selecting classification algorithms with active testing. In: Proceedings of MLDM’12
-
Leite R, Brazdil P, Vanschoren J (2012) Selecting classification algorithms with active testing. In: Proceedings of MLDM’12, pp 117–131
-
(2012)
pp 117–131
-
-
Leite, R.1
Brazdil, P.2
Vanschoren, J.3
-
46
-
-
85019317965
-
MLBCD: a machine learning tool for big clinical data
-
Luo G (2015) MLBCD: a machine learning tool for big clinical data. Health Inf Sci Syst 3:3
-
(2015)
Health Inf Sci Syst
, vol.3
, pp. 3
-
-
Luo, G.1
-
47
-
-
85042031041
-
Automatically explaining machine learning prediction results: a demonstration on type 2 diabetes risk prediction
-
Luo G (2016) Automatically explaining machine learning prediction results: a demonstration on type 2 diabetes risk prediction. Health Inf Sci Syst 4:2
-
(2016)
Health Inf Sci Syst
, vol.4
, pp. 2
-
-
Luo, G.1
-
48
-
-
84963776051
-
Efficient execution methods of pivoting for bulk extraction of Entity–Attribute–Value-modeled data
-
Luo G, Frey LJ (2016) Efficient execution methods of pivoting for bulk extraction of Entity–Attribute–Value-modeled data. IEEE J Biomed Health Inform. 20(2):644–654
-
(2016)
IEEE J Biomed Health Inform.
, vol.20
, Issue.2
, pp. 644-654
-
-
Luo, G.1
Frey, L.J.2
-
49
-
-
84906784708
-
A systematic review of predictive modeling for bronchiolitis
-
Luo G, Nkoy FL, Gesteland PH, Glasgow TS, Stone BL (2014) A systematic review of predictive modeling for bronchiolitis. Int J Med Inform 83(10):691–714
-
(2014)
Int J Med Inform
, vol.83
, Issue.10
, pp. 691-714
-
-
Luo, G.1
Nkoy, F.L.2
Gesteland, P.H.3
Glasgow, T.S.4
Stone, B.L.5
-
50
-
-
84948653900
-
A systematic review of predictive models for asthma development in children
-
Luo G, Nkoy FL, Stone BL, Schmick D, Johnson MD (2015a) A systematic review of predictive models for asthma development in children. BMC Med Inform Decis Mak 15(1):99
-
(2015)
BMC Med Inform Decis Mak
, vol.15
, Issue.1
, pp. 99
-
-
Luo, G.1
Nkoy, F.L.2
Stone, B.L.3
Schmick, D.4
Johnson, M.D.5
-
51
-
-
84948649680
-
Using computational approaches to improve risk-stratified patient management: rationale and methods
-
Luo G, Stone BL, Sakaguchi F, Sheng X, Murtaugh MA (2015b) Using computational approaches to improve risk-stratified patient management: rationale and methods. JMIR Res Protoc. 4(4):e128
-
(2015)
JMIR Res Protoc.
, vol.4
, Issue.4
, pp. 128
-
-
Luo, G.1
Stone, B.L.2
Sakaguchi, F.3
Sheng, X.4
Murtaugh, M.A.5
-
52
-
-
85044687837
-
Predicting appropriate admission of bronchiolitis patients in the emergency room: rationale and methods
-
Luo G, Stone BL, Johnson MD, Nkoy FL (2016) Predicting appropriate admission of bronchiolitis patients in the emergency room: rationale and methods. JMIR Res Protoc. 5(1):e41
-
(2016)
JMIR Res Protoc.
, vol.5
, Issue.1
, pp. 41
-
-
Luo, G.1
Stone, B.L.2
Johnson, M.D.3
Nkoy, F.L.4
-
53
-
-
85049143756
-
Hoeffding races: accelerating model selection search for classification and function approximation. In: Proceedings of NIPS’93
-
Maron O, Moore AW (1993) Hoeffding races: accelerating model selection search for classification and function approximation. In: Proceedings of NIPS’93, pp 59–66
-
(1993)
pp 59–66
-
-
Maron, O.1
Moore, A.W.2
-
56
-
-
80555140075
-
Scikit-learn: machine learning in Python
-
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O et al (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830
-
(2011)
J Mach Learn Res
, vol.12
, pp. 2825-2830
-
-
Pedregosa, F.1
Varoquaux, G.2
Gramfort, A.3
Michel, V.4
Thirion, B.5
Grisel, O.6
-
57
-
-
84867832606
-
-
Proceedings of the ECML Workshop on Meta-Learning: Building Automatic Advice Strategies for Model Selection and Method Combination
-
Petrak J (2000) Fast subsampling performance estimates for classification algorithm selection. In: Proceedings of the ECML Workshop on Meta-Learning: Building Automatic Advice Strategies for Model Selection and Method Combination 2000, pp 3–14
-
(2000)
(2000) Fast subsampling performance estimates for classification algorithm selection. In
, pp. 3-14
-
-
Petrak, J.1
-
58
-
-
0013146682
-
Meta-learning by landmarking various learning algorithms. In: Proceedings of ICML’00
-
Pfahringer B, Bensusan H, Giraud-Carrier CG (2000) Meta-learning by landmarking various learning algorithms. In: Proceedings of ICML’00, pp 743–750
-
(2000)
pp 743–750
-
-
Pfahringer, B.1
Bensusan, H.2
Giraud-Carrier, C.G.3
-
59
-
-
85049138595
-
Efficient progressive sampling. In: Proceedings of KDD’99
-
Provost FJ, Jensen D, Oates T (1999) Efficient progressive sampling. In: Proceedings of KDD’99, pp 23–32
-
(1999)
pp 23–32
-
-
Provost, F.J.1
Jensen, D.2
Oates, T.3
-
60
-
-
84905981088
-
Creating value in health care through big data: opportunities and policy implications
-
Roski J, Bo-Linn GW, Andrews TA (2014) Creating value in health care through big data: opportunities and policy implications. Health Aff (Millwood) 33(7):1115–1122
-
(2014)
Health Aff (Millwood)
, vol.33
, Issue.7
, pp. 1115-1122
-
-
Roski, J.1
Bo-Linn, G.W.2
Andrews, T.A.3
-
62
-
-
84949985138
-
Taking the human out of the loop: a review of Bayesian optimization
-
Shahriari B, Swersky K, Wang Z, Adams RP, de Freitas N (2015) Taking the human out of the loop: a review of Bayesian optimization. Proc IEEE 104(1):148–175
-
(2015)
Proc IEEE
, vol.104
, Issue.1
, pp. 148-175
-
-
Shahriari, B.1
Swersky, K.2
Wang, Z.3
Adams, R.P.4
de Freitas, N.5
-
63
-
-
84869201485
-
Practical Bayesian optimization of machine learning algorithms. In: Proceedings of NIPS’12
-
Snoek J, Larochelle H, Adams RP (2012) Practical Bayesian optimization of machine learning algorithms. In: Proceedings of NIPS’12, pp 2960–2968
-
(2012)
pp 2960–2968
-
-
Snoek, J.1
Larochelle, H.2
Adams, R.P.3
-
64
-
-
84867773113
-
Sampling-based relative landmarks: systematically test-driving algorithms before choosing. In: Proceedings of EPIA’01
-
Soares C, Petrak J, Brazdil P (2001) Sampling-based relative landmarks: systematically test-driving algorithms before choosing. In: Proceedings of EPIA’01, pp 88–95
-
(2001)
pp 88–95
-
-
Soares, C.1
Petrak, J.2
Brazdil, P.3
-
65
-
-
84894647945
-
MLI: an API for distributed machine learning. In: Proceedings of ICDM’13
-
Sparks ER, Talwalkar A, Smith V, Kottalam J, Pan X, Gonzalez JE et al. (2013) MLI: an API for distributed machine learning. In: Proceedings of ICDM’13, pp 1187–1192
-
(2013)
pp 1187–1192
-
-
Sparks, E.R.1
Talwalkar, A.2
Smith, V.3
Kottalam, J.4
Pan, X.5
Gonzalez, J.E.6
-
66
-
-
84958951297
-
Automating model search for large scale machine learning. In: Proceedings of SoCC’15
-
Sparks ER, Talwalkar A, Haas D, Franklin MJ, Jordan MI, Kraska T (2015) Automating model search for large scale machine learning. In: Proceedings of SoCC’15, pp 368–380
-
(2015)
pp 368–380
-
-
Sparks, E.R.1
Talwalkar, A.2
Haas, D.3
Franklin, M.J.4
Jordan, M.I.5
Kraska, T.6
-
68
-
-
84898939805
-
Multi-task Bayesian optimization. In: Proceedings of NIPS’13
-
Swersky K, Snoek J, Adams RP (2013) Multi-task Bayesian optimization. In: Proceedings of NIPS’13, 2004–2012
-
(2013)
2004–2012
-
-
Swersky, K.1
Snoek, J.2
Adams, R.P.3
-
69
-
-
85049152780
-
-
Adams RP: Freeze-thaw Bayesian optimization
-
Swersky K, Snoek J, Adams RP (2014) Freeze-thaw Bayesian optimization. http://arxiv.org/abs/1406.3896. Accessed 20 January 2016
-
(2014)
Snoek J
-
-
Swersky, K.1
-
70
-
-
85018371540
-
Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms. In: Proceedings of KDD’13
-
Thornton C, Hutter F, Hoos HH, Leyton-Brown K (2013) Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms. In: Proceedings of KDD’13, pp 847–855
-
(2013)
pp 847–855
-
-
Thornton, C.1
Hutter, F.2
Hoos, H.H.3
Leyton-Brown, K.4
-
72
-
-
84959907672
-
Efficient hyper-parameter optimization for NLP applications. In: Proceedings of EMNLP’15
-
Wang L, Feng M, Zhou B, Xiang B, Mahadevan S (2015) Efficient hyper-parameter optimization for NLP applications. In: Proceedings of EMNLP’15, 2112–2117
-
(2015)
2112–2117
-
-
Wang, L.1
Feng, M.2
Zhou, B.3
Xiang, B.4
Mahadevan, S.5
-
74
-
-
84959419350
-
Hyperparameter search space pruning—a new component for sequential model-based hyperparameter optimization
-
Wistuba M, Schilling N, Schmidt-Thieme L (2015a) Hyperparameter search space pruning—a new component for sequential model-based hyperparameter optimization. In: Proceedings of ECML/PKDD (2) 2015, pp 104–119
-
(2015)
Proceedings of ECML/PKDD
, vol.2
, Issue.2015
, pp. 104-119
-
-
Wistuba, M.1
Schilling, N.2
Schmidt-Thieme, L.3
-
75
-
-
84962911782
-
Learning hyperparameter optimization initializations. In: Proceedings of DSAA’15
-
Wistuba M, Schilling N, Schmidt-Thieme L (2015b) Learning hyperparameter optimization initializations. In: Proceedings of DSAA’15, pp 1–10
-
(2015)
pp 1–10
-
-
Wistuba, M.1
Schilling, N.2
Schmidt-Thieme, L.3
-
77
-
-
84955448572
-
Efficient transfer learning method for automatic hyperparameter tuning. In: Proceedings of AISTATS’14
-
Yogatama D, Mann G (2014) Efficient transfer learning method for automatic hyperparameter tuning. In: Proceedings of AISTATS’14, pp 1077–1085
-
(2014)
pp 1077–1085
-
-
Yogatama, D.1
Mann, G.2
-
78
-
-
85085251984
-
Stoica I
-
In, Proceedings of HotCloud
-
Zaharia M, Chowdhury M, Franklin MJ, Shenker S, Stoica I (2010) Spark: cluster computing with working sets. In: Proceedings of HotCloud 2010
-
(2010)
Spark: cluster computing with working sets
, pp. 2010
-
-
Zaharia, M.1
Chowdhury, M.2
Franklin, M.J.3
Shenker, S.4
|