Sequential Parameter Optimization Toolbox (SPOT) google
The performance of optimization algorithms relies crucially on their parameterizations. Finding good parameter settings is called algorithm tuning. Using a simple simulated annealing algorithm, we will demonstrate how optimization algorithms can be tuned using the sequential parameter optimization toolbox (SPOT). SPOT provides several tools for automated and interactive tuning. The underling concepts of the SPOT approach are explained. This includes key techniques such as exploratory fitness landscape analysis and response surface methodology. Many examples illustrate how SPOT can be used for understanding the performance of algorithms and gaining insight into algorithm’s behavior. Furthermore, we demonstrate how SPOT can be used as an optimizer and how a sophisticated ensemble approach is able to combine several meta models via stacking. …

Cognitive Computing google
Cognitive computing refers to the development of computer systems modeled after the human brain. Originally referred to as artificial intelligence, researchers began to use the modern term instead in the 1990s, to indicate that the science was designed to teach computers to think like a human mind, rather than developing an artificial system. This type of computing integrates technology and biology in an attempt to re-engineer the brain, one of the most efficient and effective computers on Earth.
Cognitive computing is a way of processing data that is neither linear nor deterministic. It uses the ideas behind neuroscience and psychology to augment human reasoning with better pattern matching while determining the optimal information a person needs to make decisions. Cognitive computing is different than other forms of software. Instead of shepherding data through pre-determined pathways, it finds the previously unknown paths and patterns through the data. This is ultimately a more scalable model than relying on experts to synthesize data since there are too few experts of any sort available at any one time. Cognitive computing doesn’t try to fit data into an existing model; it looks at the data and figures out what the model is first.
Cognitive Computing
Cognitive Computing: Solving the Big Data Problem?
Cognitive Computing Defined

Cross-Entropy Clustering google
We build a general and easily applicable clustering theory, which we call crossentropy clustering (shortly CEC), which joins the advantages of classical kmeans (easy implementation and speed) with those of EM (a ne invariance and ability to adapt to clusters of desired shapes). Moreover, contrary to k-means and EM, CEC nds the optimal number of clusters by automatically removing groups which have negative information cost. Although CEC, like EM, can be build on an arbitrary family of densities, in the most important case of Gaussian CEC the division into clusters is a ne invariant.
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