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. Machine Learning Vol. 34. Nos 1–3. 43–69. Dagan I. Similarity-based Models of Word Co-occurrence Probabilities Machine Learning

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Orvosi Hetilap
Authors: Tamás Schneider, Erika Tóth, József Lővey, Zsuzsanna Molnár, Beáta Deák, Erika Várady, Judit Csomor, András Matolcsy, Zsolt Lengyel, Klára Petri, István Gaudi, and András Rosta

862 Shipp, M. A., Ross, K. N., Tamayo, P. és mtsai: Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nature Med

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Imaging
Authors: Melinda Boussoussou, Borbála Vattay, Bálint Szilveszter, Márton Kolossváry, Judit Simon, Milán Vecsey-Nagy, Béla Merkely, and Pál Maurovich-Horvat

, Tesche C , De Geer J , Kurata A , Diagnostic Accuracy of a Machine-Learning Approach to Coronary Computed Tomographic Angiography-Based Fractional Flow Reserve: Result From the MACHINE Consortium . Circ Cardiovasc Imaging . 2018 ; 11 ( 6 ): e

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Langley, P. 1988. Trading off simplicity and coverage in incremental concept learning. Proc. 5th Internatl. Conf. Machine Learning, Ann Arbor, Morgan Kaufman, CA. pp. 73-86. Trading off simplicity and coverage in incremental

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Machine Learning Research Vol. 16 . 3905 – 3909 . http://jmlr.org/papers/v16/hothorn15a.html Hothorn , T. & Zeileis , A. 2016 . Package ‘Partykit’: CRAN . https

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. Artificial intelligence in drug discovery. Future Med Chem. 2018; 10: 2025–2028. 48 Vamathevan J, Clark D, Czodrowsky P, et al. Applications of machine learning in drug discovery and

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–533. Sun, D., van Erp, T. G., Thompson, P. M., Bearden, C. E., Daley, M., Kushan, L., et al. (2009a). Elucidating a magnetic resonance imaging-based neuroanatomic biomarker for psychosis: classification analysis using probabilistic brain atlas and machine

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How much is intellectual capital worth for the organization?

Separating the measurement and evaluation of intellectual capital elements with evaluator functions at EMS companies

Acta Oeconomica
Authors: János Kövesi, Zsuzsanna Tóth, and Tamás Jónás

347 360 Mitchell, T. (1997): Machine Learning . New York: McGraw-Hill. Mitchell T

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Conf. Machine Learning ECML-94, Catalina, Italy. Springer, Berlin, pp. 49-67. A context similarity measure 49 67

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International Conference on Machine Learning , Morgan Kaufmann, San Francisco, CA. pp. 543-550. Bayesian temporal data clustering using hidden Markov model representation 543 550

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