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Semisupervised Learning of Classifiers: Theory, Algorithms, and Their Application to Human-Computer Interaction.

, , , , и . IEEE Trans. Pattern Anal. Mach. Intell., 26 (12): 1553-1567 (2004)

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Hanie Iraki

Huschang Irannejad

Homi Irani

Paul Irawan

Parviz Irani

 

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Authentic Facial Expression Analysis., , , , , и . FGR, стр. 517-522. IEEE Computer Society, (2004)Correlating Instrumentation Data to System States: A Building Block for Automated Diagnosis and Control., , , , и . OSDI, стр. 231-244. USENIX Association, (2004)Machine Learning in Computer Vision, , , и . Computational Imaging and Vision Springer, (2005)Real-time anomaly detection system for time series at scale., , , и . ADF@KDD, том 71 из Proceedings of Machine Learning Research, стр. 56-65. PMLR, (2017)Properties and Benefits of Calibrated Classifiers., и . PKDD, том 3202 из Lecture Notes in Computer Science, стр. 125-136. Springer, (2004)Learning from Little: Comparison of Classifiers Given Little Training., и . PKDD, том 3202 из Lecture Notes in Computer Science, стр. 161-172. Springer, (2004)Unlabeled Data Can Degrade Classification Performance of Generative Classifiers, и . HPL-2001-234. HP Laboratories Palo Alto, (2001)Towards authentic emotion recognition., , , , и . SMC (1), стр. 623-628. IEEE, (2004)Semi-supervised network traffic classification., , , , и . SIGMETRICS, стр. 369-370. ACM, (2007)Beware the Null Hypothesis: Critical Value Tables for Evaluating Classifiers., и . ECML, том 3720 из Lecture Notes in Computer Science, стр. 133-145. Springer, (2005)