parallel geographically weighted k-nearest neighbor classifier (CH Instruments)
90
Structured Review
CH Instruments
parallel geographically weighted k-nearest neighbor classifier
Parallel Geographically Weighted K Nearest Neighbor Classifier, supplied by CH Instruments, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/parallel+geographically+weighted+k-nearest+neighbor+classifier/parallel+geographically+weighted+k+nearest+neighbor+classifier/10__1007_slash_s00521___021___06004___8-446-18-4
Average 90 stars, based on 1 article reviews
Parallel Geographically Weighted K Nearest Neighbor Classifier, supplied by CH Instruments, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/parallel+geographically+weighted+k-nearest+neighbor+classifier/parallel+geographically+weighted+k+nearest+neighbor+classifier/10__1007_slash_s00521___021___06004___8-446-18-4
Average 90 stars, based on 1 article reviews
parallel geographically weighted k-nearest neighbor classifier - by Bioz Stars,
2026-10
90/100 stars
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other:Article Title: Prediction of cement-based mortars compressive strength using machine learning techniques Article Snippet: The application of artificial neural networks in mapping the mechanical characteristics of the cement-based materials is underlined in previous investigations.. However, this machine learning technique includes several major deficiencies highlighted in the literature, such as the overfitting problem and the inability to explain the decisions.. Hence, the present study investigates the applicability of other commonmachine learning techniques, i.e., support vectormachine, random forest (RF), decision tree, AdaBoost and k-nearest neighbors in mapping the behavior of the compressive strength (CS) of cement-based mortars. |