If the dataset available to machine learning results from cluster sampling (e.g. patients from a sample of hospital wards), the usual cross-validation error rate estimate can lead to biased and misleading results. An adapted cross-validation is described for this case. Using a simulation, the sampling distribution of the generalization error rate estimate, under cluster or simple random samplin...

This paper presents a data-driven cluster sampling framework for parsing scene images into generic regions (such as the sky, mountain and water) and objects (such as cows, horses and cars). We adopt generative models for both generic regions and objects, thus their likelihood probabilities are comparable and are learned under a common information projection principle. The inference algorithm fo...

Cluster stability research is involved with the validity of clusters generated by a clustering algorithm. In other words, it answers whether generated clusters are true clusters or due to chance. Estimating true numbers of clusters is related to this problem, since often the cluster validity is based on this estimate. In the literature, there are a number of methods available for both purposes....

1. What is a Survey? 2. Probability sampling 3. Common probability sampling designs 3.1. Simple Random Sampling 3.2. Stratified Sampling 3.3. Cluster Sampling 3.4. Unequal Probability Sampling 3.5. Systematic Sampling 3.6. Stratified Multistage Sampling 4. Survey estimates and standard errors 5. Nonsampling errors 6. Sampling rare populations 7. Issues in Survey Design Acknowledgments Glossary ...

In many surveys, characteristic of interest is sparsely distributed but highly aggregated; in such situations the adaptive cluster sampling is very useful. Examples of such populations can be found in fisheries, mineral investigations (unevenly distributed ore concentrations), animal and plant populations (rare and endangered species), pollution concentrations and hot spot investigations, and e...