Ranking Model Real-Time Adaptation via Preference Learning Based on Dynamic Clustering

Lyubchyk, LeonidGaluza, OleksyGrinberg, Galina2018-06-212018-06-212017Lyubchyk L. M. Ranking Model Real-Time Adaptation via Preference Learning Based on Dynamic Clustering / L. M. Lyubchyk, A. A. Galuza, G. L. Grinberg // Системний аналiз та iнформацiйнi технологiї = System analysis and information technology : матерiали 19-ї Мiжнар. наук.-техн. конф. SAIT 2017, 22-25 травня 2017 р. – Київ : ННК "IПСА" НТУУ "КПI iм. Iгоря Сiкорського", 2017. – С. 12.https://repository.kpi.kharkov.ua/handle/KhPI-Press/36819The proposed preference learning on clusters method allows to fully realizing the advantages of the kernel-based approach. While the dimension of the model is determined by a pre-selected number of clusters and its complexity do not grow with increasing number of observations. Thus real-time preference function identification algorithm based on training data stream includes successive estimates of cluster parameter as well as average cluster ranks updating and recurrent kernel-based nonparametric estimation of preference model.enpreference functionkernel machineclusteringranking learningRanking Model Real-Time Adaptation via Preference Learning Based on Dynamic ClusteringThesishttps://orcid.org/0000-0002-0774-5414