Results 11 to 20 of about 323,408 (293)
Unsupervised two-class and multi-class support vector machines for abnormal traffic characterization [PDF]
, 2009 Although measurement-based real-time traffic classification has received considerable research attention, the timing constraints imposed by the high accuracy requirements and the learning phase of the algorithms employed still remain a challenge. In this
Hutchison, D. +3 more
core +7 more sources
Unsupervised Part Discovery by Unsupervised Disentanglement [PDF]
, 2021 GCPR 2020 (Oral)
Sandro Braun +2 more
openaire +4 more sources
Unsupervised two-class & multi-class support vector machines for abnormal traffic characterization. [PDF]
, 2009 Although measurement-based real-time traffic classification has received considerable research attention, the timing constraints imposed by the high accuracy requirements and the learning phase of the algorithms employed still remain a challenge. In this
Kim, Hyun-chul +7 more
core +4 more sources
Clustering Analysis for Classifying Student Academic Performance in Higher Education
Applied Sciences, 2022 There are three income categories for Malaysians: the top 20% (T20), the middle 40% (M40), and the bottom 40% (B40). The government has extended B40′s access to higher education to eliminate socioeconomic disparities and improve their lives.
Ahmad Fikri Mohamed Nafuri +4 more
doaj +1 more source
Two-pass decision tree construction for unsupervised adaptation of HMM-based synthesis models [PDF]
, 2010 Hidden Markov model (HMM) -based speech synthesis systems possess several advantages over concatenative synthesis systems. One such advantage is the relative ease with which HMM-based systems are adapted to speakers not present in the training dataset ...
core +9 more sources
Dimensionality Reduction by Similarity Distance-Based Hypergraph Embedding
Atmosphere, 2022 Dimensionality reduction (DR) is an essential pre-processing step for hyperspectral image processing and analysis. However, the complex relationship among several sample clusters, which reveals more intrinsic information about samples but cannot be ...
Xingchen Shen, Shixu Fang, Wenwen Qiang
doaj +1 more source
CoRR, 2021 This paper studies "unsupervised finetuning", the symmetrical problem of the well-known "supervised finetuning". Given a pretrained model and small-scale unlabeled target data, unsupervised finetuning is to adapt the representation pretrained from the source domain to the target domain so that better transfer performance can be obtained.
Suichan Li +7 more
openaire +2 more sources
American Journal of Orthodontics and Dentofacial Orthopedics, 2023 This work was supported by the Flemish Government under the “Onderzoeksprogramma Artifici€ele Intelligentie (AI) Vlaanderen ...
Dirk Valkenborg +3 more
openaire +3 more sources
Unsupervised learning of generative topic saliency for person re-identification [PDF]
, 2014 (c) 2014. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.© 2014. The copyright of this document resides with its authors.
Wang, H +5 more
core +5 more sources
ENTROPY BASED GREEDY UNSUPERVISED FEATURE SELECTION METHOD USING ROUGH SET THEORY FOR CLASSIFICATION
ICTACT Journal on Soft Computing, 2022 Feature selection technique attempts to select and remove irrelevant features while ensuring that an informative subset of features remains in the dataset.
Rubul Kumar Bania, Satyajit Sarmah
doaj +1 more source