Könyv Machine learning classifiers &Classifier ensample Rania Ibrahim

Machine learning classifiers &Classifier ensample

Nyelv: Angol
Kötés: Puha kötésű
Elérhetőség: Beszállítói készleten
Küldés 5-8 napon belül
15 070 Ft
There are an emergent machine learning(ML) algorithms to classify land-cover and land-use. In this b...

Információk a könyvről

Nyelv
Angol
Kötés
Könyv - Puha kötésű
Kiadva
2023
oldal
52
EAN
9786206845867
ISBN
6206845869
Enbook ID
44618565
Súly
96
Méretek
150 x 220 x 4

Teljes leírás

There are an emergent machine learning(ML) algorithms to classify land-cover and land-use. In this book we focus on the relatively mature methods (seven methods) support vector (SVM) machines, decision trees (DTs), artificial neural networks, k-nearest neighbours (k-NN), naïve Bayes, Boosting and Random forest (RF).Accurate and timely collection of urban land use and land cover information is crucial for many aspects of urban development and environment protection.Accurate land covers classification is challenging. Improving land cover classification is a hot topic. It is needed for many applications such as land use land cover mapping environmental monitoring, natural resource management, urban planning, and management and change detection. Then a number of ensample methods were studied to combine various classifiers.

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