Swapan Talukdar
Global and Planetary Change · 2020–2021
A researcher in the Science corpus: 4 papers; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Global and Planetary Change — 34 citations into it
- Atmospheric Science — 18 citations into it
- Environmental Engineering — 10 citations into it
Whom Swapan Talukdar trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Andy Liaw
- Ajay Mathur
- Mariette Awad
- Zekâi Sen
- Trevor J. Hastie
- Giorgos Mountrakis
- Chengquan Huang
- Aaron E. Maxwell
- Pranab Kumar Sen
- Leo Breiman
Papers from Swapan Talukdar's perspective
Ordered by how strongly the researchers Swapan Talukdar trusts endorse each paper. Same corpus, a different order for every researcher.
- Random Forests
- An assessment of support vector machines for land cover classification
- Support vector machines in remote sensing: A review
- Classification and Regression by randomForest
- Selection of classification techniques for land use - land cover change investigation
- Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers
- The Nature of Statistical Learning Theory
- Trend Identification Simulation and Application
- PCA‐based land‐use change detection and analysis using multitemporal and multisensor satellite data
- Multiclass and Binary SVM Classification: Implications for Training and Classification Users (IEEE Geoscience and Remote Sensing Letters)
Their papers
- Modeling fragmentation probability of land-use and land-cover using the bagging, random forest and random subspace in the Teesta River Basin, Bangladesh (2021) — 129 citations
- Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations - A Review (2020) — 1,016 citations
- Analyzing trend and forecasting of rainfall changes in India using non-parametrical and machine learning approaches (2020) — 346 citations
- Flood susceptibility modelling using advanced ensemble machine learning models (2020) — 483 citations