Adaptive Learning Rate (ADADELTA)
Automatically set learning rate for each neuron in a neural network based on it´s training history. …

Clustering Validation Indices
The purpose of clustering is to determine the intrinsic grouping in a set of unlabeled data, where the objects in each group are indistinguishable under some criterion of similarity. Clustering is an unsupervised classification process fundamental to data mining (one of the most important tasks in data analysis). It has applications in several fields like bioinformatics, web data analysis, text mining and scientific data exploration. Clustering refers to unsupervised learning and, for that reason it has no a priori data set information. However, to get good results, the clustering algorithm depends on input parameters. For instance, k-means and CURE algorithms require a number of clusters (k) to be created. In this sense, the question is: What is the optimal number of clusters? Currently, cluster validity indexes research has drawn attention as a means to give a solution. Many different cluster validity methods have been proposed without any a priori class information. Clustering validation is a technique to find a set of clusters that best fits natural partitions (number of clusters) without any class information. Generally speaking, there are two types of clustering techniques, which are based on external criteria and internal criteria.
• External validation: Based on previous knowledge about data.
• Internal validation: Based on the information intrinsic to the data alone.
If we consider these two types of cluster validation to determine the correct number of groups from a dataset, one option is to use external validation indexes for which a priori knowledge of dataset information is required, but it is hard to say if they can be used in real problems (usually, real problems do not have prior information of the dataset in question). Another option is to use internal validity indexes which do not require a priori information from dataset. …

Repeated Measures
Repeated measures design uses the same subjects with every branch of research, including the control. For instance, repeated measurements are collected in a longitudinal study in which change over time is assessed. Other (non-repeated measures) studies compare the same measure under two or more different conditions. For instance, to test the effects of caffeine on cognitive function, a subject’s math ability might be tested once after they consume caffeine and another time when they consume a placebo.
…