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Adaptive Vocabulary Construction for Frustration Intensity Modelling in Customer Support

, and . International Journal of Computer Science & Information Technology (IJCSIT), 12 (6): 01 - 15 (December 2020)
DOI: 10.5121/ijcsit.2020.12603

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Modelling of Adequate Costs of Utilities Services., , , and . ICIST, volume 639 of Communications in Computer and Information Science, page 3-17. (2016)CN2-R: Faster CN2 with randomly generated complexes.. MMAR, page 306-309. IEEE, (2011)Naive Augmenting Q-Learning to Process Feature-Based Representations of States.. ESANN, (2014)Spiking Neural Networks to Detect Temporal Patterns.. DB&IS, volume 187 of Frontiers in Artificial Intelligence and Applications, page 131-142. IOS Press, (2008)Adaptive Vocabulary Construction for Frustration Intensity Modelling in Customer Support, and . International Journal of Computer Science & Information Technology (IJCSIT), 12 (6): 01 - 15 (December 2020)Adaptive Vocabulary Construction for Frustration Intensity Modelling in Customer Support Dialog Texts, and . International Journal of Computer Science & Information Technology (IJCSIT), 12 (6): 01 - 15 (December 2020)Performance Measurement Framework with Indicator Life-cycle Support., , and . DB&Local Proceedings, volume 924 of CEUR Workshop Proceedings, page 115-127. CEUR-WS.org, (2012)An Extension of Multi-Layer Perceptron Based on Layer-Topology.. IEC (Prague), page 178-181. Enformatika, Çanakkale, Turkey, (2005)Random Spikes to Enhance Learning in Spiking Neural Networks.. DB&IS, volume 224 of Frontiers in Artificial Intelligence and Applications, page 369-379. IOS Press, (2010)Near Real-Time Data Warehousing with Multi-stage Trickle and Flip.. BIR, volume 90 of Lecture Notes in Business Information Processing, page 73-82. Springer, (2011)