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Tom Arjannikov
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2020 – today
- 2021
- [b1]Tom Arjannikov:
Positive unlabeled learning applications in music and healthcare. University of Victoria, Canada, 2021 - [c9]Tom Arjannikov
, George Tzanetakis:
Cold-Start Hospital Length of Stay Prediction Using Positive-Unlabeled Learning. BHI 2021: 1-4 - [c8]Tom Arjannikov
, George Tzanetakis:
An Empirical Investigation of PU Learning for Predicting Length of Stay. ICHI 2021: 41-47 - 2020
- [j2]Khobaib Zaamout
, Tom Arjannikov
, Ken Barker
:
A Social Crowdsourcing Community Case Study: Interaction Patterns, Evolution, and Factors That Affect Them. IEEE Trans. Comput. Soc. Syst. 7(3): 659-671 (2020) - [j1]Khobaib Zaamout
, Tom Arjannikov
, Ken Barker
:
Corrections to "A Social Crowdsourcing Community Case Study: Interaction Patterns, Evolution, and Factors That Affect Them". IEEE Trans. Comput. Soc. Syst. 7(5): 1317 (2020)
2010 – 2019
- 2017
- [c7]Tom Arjannikov, Simon Diemert, Sudhakar Ganti, Chloe Lampman, Edward C. Wiebe:
Using Markov Chains to Model Sensor Network Reliability. ARES 2017: 6:1-6:10 - [c6]Tom Arjannikov, George Tzanetakis
:
Histogram-Based Asymmetric Relabeling for Learning from Only Positive and Unlabeled Data. ICMLA 2017: 1065-1070 - 2016
- [c5]Moyuan Huang, Wenge Rong, Tom Arjannikov, Nan Jiang, Zhang Xiong:
Bi-Modal Deep Boltzmann Machine Based Musical Emotion Classification. ICANN (2) 2016: 199-207 - [c4]Tom Arjannikov, John Z. Zhang:
Do Nested Dichotomies Help in Automatic Music Genre Classification? An Empirical Study. ICMC 2016 - 2015
- [c3]Tom Arjannikov, John Z. Zhang:
An Empirical Study on Structured Dichotomies in Music Genre Classification. ICMLA 2015: 493-496 - 2014
- [c2]Tom Arjannikov, John Z. Zhang:
An Association-based Approach to Genre Classification in Music. ISMIR 2014: 95-100 - 2013
- [c1]Tom Arjannikov, Chris Sanden, John Z. Zhang:
Verifying Music Tag Annotation Via Association Analysis. ISMIR 2013: 195-200
Coauthor Index
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