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Igor Kononenko

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2008
48EEErik Strumbelj, Igor Kononenko: Towards a Model Independent Method for Explaining Classification for Individual Instances. DaWaK 2008: 273-282
47EEZoran Bosnic, Igor Kononenko: Empirical Analysis of Reliability Estimates for Individual Regression Predictions. DaWaK 2008: 379-388
46EEZoran Bosnic, Igor Kononenko: Estimation of individual prediction reliability using the local sensitivity analysis. Appl. Intell. 29(3): 187-203 (2008)
45EEZoran Bosnic, Igor Kononenko: Comparison of approaches for estimating reliability of individual regression predictions. Data Knowl. Eng. 67(3): 504-516 (2008)
44EEMarko Robnik-Sikonja, Igor Kononenko: Explaining Classifications For Individual Instances. IEEE Trans. Knowl. Data Eng. 20(5): 589-600 (2008)
2006
43EEMatjaz Bevk, Igor Kononenko: Towards symbolic mining of images with association rules: Preliminary results on textures. Intell. Data Anal. 10(4): 379-393 (2006)
2005
42EELuka Sajn, Matjaz Kukar, Igor Kononenko, Metka Milcinski: Automatic Segmentation of Whole-Body Bone Scintigrams as a Preprocessing Step for Computer Assisted Diagnostics. AIME 2005: 363-372
41EEIgor Kononenko, Miha Sedej, Aleksander Sadikov: GDV Measures Vitality? CBMS 2005: 443-445
40 Zoran Bosnic, Igor Kononenko: Estimation of Prediction Reliability in Regression Based on a Transductive Approach. IICAI 2005: 3502-3516
39EELuka Sajn, Matjaz Kukar, Igor Kononenko, Metka Milcinski: Computerized segmentation of whole-body bone scintigrams and its use in automated diagnostics. Computer Methods and Programs in Biomedicine 80(1): 47-55 (2005)
38EEAleksander Sadikov, Ivan Bratko, Igor Kononenko: Bias and pathology in minimax search. Theor. Comput. Sci. 349(2): 268-281 (2005)
2003
37 Aleksander Sadikov, Ivan Bratko, Igor Kononenko: Search versus Knowledge: An Empirical Study of Minimax on KRK. ACG 2003: 33-44
36EEMarko Robnik-Sikonja, David Cukjati, Igor Kononenko: Comprehensible evaluation of prognostic factors and prediction of wound healing. Artificial Intelligence in Medicine 29(1-2): 25-38 (2003)
35EEMarko Robnik-Sikonja, Igor Kononenko: Theoretical and Empirical Analysis of ReliefF and RReliefF. Machine Learning 53(1-2): 23-69 (2003)
2002
34EEMatjaz Bevk, Igor Kononenko: A Statistical Approach to Texture Description of Medical Images: A Preliminary Study. CBMS 2002: 239-240
33EEMatjaz Kukar, Igor Kononenko: Reliable Classifications with Machine Learning. ECML 2002: 219-231
2001
32EEMarko Robnik-Sikonja, David Cukjati, Igor Kononenko: Evaluation of Prognostic Factors and Prediction of Chronic Wound Healing Rate by Machine Learning Tools. AIME 2001: 77-87
31 Marko Robnik-Sikonja, Igor Kononenko: Comprehensible Interpretation of Relief's Estimates. ICML 2001: 433-440
30 Igor Kononenko: Machine learning for medical diagnosis: history, state of the art and perspective. Artificial Intelligence in Medicine 23(1): 89-109 (2001)
1999
29 Marko Robnik-Sikonja, Igor Kononenko: Attribute Dependencies, Understandability and Split Selection in Tree Based Models. ICML 1999: 344-353
28 Matjaz Kukar, Igor Kononenko, Ciril Groselj, Katarina Kralj, Jure Fettich: Analysing and improving the diagnosis of ischaemic heart disease with machine learning. Artificial Intelligence in Medicine 16(1): 25-50 (1999)
1998
27 Matjaz Kukar, Igor Kononenko: Cost-Sensitive Learning with Neural Networks. ECAI 1998: 445-449
26 Marko Robnik-Sikonja, Igor Kononenko: Pruning Regression Trees with MDL. ECAI 1998: 455-459
25 Igor Kononenko: The Minimum Description Length Based Decision Tree Pruning. PRICAI 1998: 228-237
24 Nada Lavrac, Blaz Zupan, Igor Kononenko, Matjaz Kukar, Elpida T. Keravnou: Intelligent Data Analysis for Medical Diagnosis: Using Machine Learning and Temporal Abstraction. AI Commun. 11(3-4): 191-218 (1998)
23EEUros Pompe, Igor Kononenko: Efficient Induction and Effective Use of First-Order Knowledge. Applied Artificial Intelligence 12(5): 421-453 (1998)
22EESamo Zorc, D. Noe, Igor Kononenko: Efficient Derivation of the Optimal Assembly Sequence from Product Description. Cybernetics and Systems 29(2): 159-179 (1998)
1997
21 Igor Zelic, Igor Kononenko, Nada Lavrac, Vanja Vuga: Machine Learning Applied to Diagnosis of Sport Injuries. AIME 1997: 138-141
20 Matjaz Kukar, Ciril Groselj, Igor Kononenko, Jure Fettich: An Application of Machine Learning in the Diagnosis of Ischaemic Heart Disease. AIME 1997: 461-464
19EEIgor Zelic, Igor Kononenko, Nada Lavrac, Vanja Vuga: Diagnosis of sport injuries with machine learning: explanation of induced decisions. CBMS 1997: 195-199
18EEMatjaz Kukar, Ciril Groselj, Igor Kononenko, Jure Fettich: An application of machine learning in the diagnosis of ischaemic heart disease. CBMS 1997: 70-75
17 Marko Robnik-Sikonja, Igor Kononenko: An adaptation of Relief for attribute estimation in regression. ICML 1997: 296-304
16 Uros Pompe, Igor Kononenko: Probabilistic First-Order Classification. ILP 1997: 235-242
15 Igor Kononenko, Edvard Simec, Marko Robnik-Sikonja: Overcoming the Myopia of Inductive Learning Algorithms with RELIEFF. Appl. Intell. 7(1): 39-55 (1997)
1996
14 Ivan Bratko, Bojan Cestnik, Igor Kononenko: Attribute-Based Learning. AI Commun. 9(1): 27-32 (1996)
13 Matjaz Kukar, Igor Kononenko, T. Silvester: Machine learning in prognosis of the femoral neck fracture recovery. Artificial Intelligence in Medicine 8(5): 431-451 (1996)
12 Igor Kononenko: On Facts Versus Misconceptions about Rough Sets. Informatica (Slovenia) 20(4): (1996)
1995
11 Igor Kononenko: On Biases in Estimating Multi-Valued Attributes. IJCAI 1995: 1034-1040
1994
10 Igor Kononenko: Estimating Attributes: Analysis and Extensions of RELIEF. ECML 1994: 171-182
9 Igor Kononenko: On Bayesian Neural Networks. Informatica (Slovenia) 18(2): (1994)
8 Igor Kononenko, Samo Zorc: Critical Analysis of Rough Sets Approach to Machine Learning. Informatica (Slovenia) 18(3): (1994)
1993
7 Igor Kononenko: Inductive and Bayesian learning in medical diagnosis. Applied Artificial Intelligence 7(4): 317-337 (1993)
6 Igor Kononenko: Successive Naive Bayesian Classifier. Informatica (Slovenia) 17(2): (1993)
1992
5 Igor Kononenko: Combining Decisions of Multiple Rules. AIMSA 1992: 87-96
4 Igor Kononenko, Matevz Kovacic: Learning as Optimization: Stochastic Generation of Multiple Knowledge. ML 1992: 257-262
1991
3 Igor Kononenko: Semi-Naive Bayesian Classifier. EWSL 1991: 206-219
2 Igor Kononenko, Ivan Bratko: Information-Based Evaluation Criterion for Classifier's Performance. Machine Learning 6: 67-80 (1991)
1987
1 Bojan Cestnik, Igor Kononenko, Ivan Bratko: ASSISTANT 86: A Knowledge-Elicitation Tool for Sophisticated Users. EWSL 1987: 31-45

Coauthor Index

1Matjaz Bevk [34] [43]
2Zoran Bosnic [40] [45] [46] [47]
3Ivan Bratko [1] [2] [14] [37] [38]
4Bojan Cestnik [1] [14]
5David Cukjati [32] [36]
6Jure Fettich [18] [20] [28]
7Ciril Groselj [18] [20] [28]
8Elpida T. Keravnou [24]
9Matevz Kovacic [4]
10Katarina Kralj [28]
11Matjaz Kukar [13] [18] [20] [24] [27] [28] [33] [39] [42]
12Nada Lavrac [19] [21] [24]
13Metka Milcinski [39] [42]
14D. Noe [22]
15Uros Pompe [16] [23]
16Marko Robnik-Sikonja [15] [17] [26] [29] [31] [32] [35] [36] [44]
17Aleksander Sadikov [37] [38] [41]
18Luka Sajn [39] [42]
19Miha Sedej [41]
20T. Silvester [13]
21Edvard Simec [15]
22Erik Strumbelj [48]
23Vanja Vuga [19] [21]
24Igor Zelic [19] [21]
25Samo Zorc [8] [22]
26Blaz Zupan [24]

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Copyright © Sun May 17 03:24:02 2009 by Michael Ley (ley@uni-trier.de)