Collaborating Authors

Knowledge-Based Morphological Classification of Galaxies from Vision Features

AAAI Conferences

This paper presents a knowledge-based approach to the task of learning and identifying galaxies from their images. To this effect, we propose a crowd-sourced pipeline approach that employs two systems - case based and rule based systems. First, the approach extracts morphological features i.e. features describing the structure of the galaxy such as its shape, central characteristics e.g., has a bar or bulge at its center)etc., using computer vision techniques. Then it employs a case based reasoning system and a rule based system to perform the classification task. Our initial results show that this pipeline is effective in learning reasonably accurate models on this complex task.

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What Defines The Upper Class? No Consensus On Who's Considered Rich In America

International Business Times

If you ask Americans who should be considered wealthy, you'll get many different answers. But one thread ties those responses together, and it can be paraphrased this way: "If they make more money than I do, then they're rich." In other words, people with household incomes of less than $50,000 believed people who earned $200,000 a year would be considered rich, according to a Washington Post poll. But people who made six-figure salaries said Americans shouldn't be considered rich unless they were making half a million dollars annually. Of course, that definition is subjective -- an interpretation based on personal experience instead of data.

Develop an Intuition for Severely Skewed Class Distributions


An imbalanced classification problem is a problem that involves predicting a class label where the distribution of class labels in the training dataset is not equal. A challenge for beginners working with imbalanced classification problems is what a specific skewed class distribution means. For example, what is the difference and implication for a 1:10 vs. a 1:100 class ratio? Differences in the class distribution for an imbalanced classification problem will influence the choice of data preparation and modeling algorithms. Therefore it is critical that practitioners develop an intuition for the implications for different class distributions.

Generating classes at runtime and invoking their methods in Java


The generation of classes at runtime is an advanced topic that requires a lot of knowledge that can be reduced if you use particular libraries that perform the most complex functions to accomplish this task. So, for this purpose, we can use the ClassFactory component and the sources generating components of the Burningwave Core library. Once the sources have been set in UnitSourceGenerator objects, they must be passed to loadOrBuildAndDefine method of ClassFactory with the ClassLoader where you want to define newly generated classes. This method performs the following operations: tries to load all the classes present in the UnitSourceGenerator through the class loader, if at least one of these is not found it proceeds to compile all the UnitSourceGenerators and uploading their classes on class loader: in this case, keep in mind that if a class with the same name was previously loaded by the class loader, the compiled class will not be uploaded. Once the classes have been compiled and loaded, it is possible to invoke their methods in several ways, as shown at the end of the example below.