An automated Web portal template generation method includes parsing, via a parser subsystem, a number of Webpages of a first Website from which a Web portal template to be customized is to be accessed. The method further includes producing an entity feature set for the first Website based on a result of the parsing and processing the entity feature set for the first Website via a classifier subsystem to produce a set of data that represents, for each of a plurality of entities, a respective probability of the entity belonging to a respective one of a plurality of classes. The method additionally includes performing, by a color matching subsystem, color matching on the set of data produced by the classifier subsystem to generate a number of proposed color combinations for a proposed customization of the Web portal template.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. (canceled) 2. A method of operation in a training system related to automated customization of Web portal templates via machine-learning, the method comprising: generating, via at least one processor-based component of the training system, an initial positive training data set of based on a plurality of Websites of entities in a first defined field of endeavor; generating, via at least one processor-based component of the training system, an initial negative training data set of based on a plurality of Websites of entities in one or more fields of endeavor that are different from the first defined field of endeavor; and training, via at least one processor-based component of the training system, a classifier based on the initial positive data set and the initial negative data sets. 3. The method of claim 2, further comprising: eliminating, via at least one processor-based component of the training system, at least one entity which appears in both the initial positive training data set and the initial negative training data set before training the classifier based on the initial positive data set and the initial negative data sets. 4. The method of claim 2, further comprising: generating a classification feature set via at least one processor-based component of the training system. 5. The method of claim 4, further comprising: training an algorithm based at least in part on the generated classification feature set via at least one processor-based component of the training system. 6. The method of claim 5 wherein training an algorithm includes training a decision tree classifier. 7. The method of claim 2, further comprising: for each of the plurality of Websites of the entities in a first defined field of endeavor, crawling an n-level deep page tree of respective ones of the Websites; and parsing, via at least one processor-based component of the training system, a number of Webpages of the respective Websites; and for each of the plurality of Websites of the entities in the or more fields of endeavor that are different from the first defined field of endeavor, crawling an n-level deep page tree of the respective ones of the Websites; and parsing, via at least one processor-based component of the training system, a number of Webpages of the respective Websites. 8. The method of claim 7, further comprising: identifying potential areas of styling in the Webpages that comprise the respective Websites; and extracting cascade style sheet (CSS) information from at least some of the identified potential areas of styling. 9. The method of claim 7 wherein the results of parsing include a pair collection of color and corresponding feature vector, the feature vectors comprising an n-dimensional vector of numerical values for respective attributes associated with the respective color in the respective Websites. 10. An automated Web portal template customization machine-learning training system, the system comprising: at least one processor; at least one non-transitory storage media communicably coupled to the at least one processor, the non-transitory storage media storing at least one of data or processor-readable instruction sets that, when executed by the at least one processor, cause the at least one processor to function as an automated Web portal template customization machine-learning training system that: generates an initial positive training data set of based on a plurality of Websites of entities in a first defined field of endeavor; generates an initial negative training data set of based on a plurality of Websites of entities in one or more fields of endeavor that are different from the first defined field of endeavor; and trains a classifier based on the initial positive data set and the initial negative data sets. 11. The automated Web portal template customization machine-learning training system of claim 10, wherein the processor-readable instruction sets further cause the at least one processor to function as an automated Web portal template customization machine-learning training system that: eliminates at least one entity which appears in both the initial positive training data set and the initial negative training data set before training the classifier based on the initial positive data set and the initial negative data sets. 12. The automated Web portal template customization machine-learning training system of claim 10 wherein the processor-readable instruction sets further cause the at least one processor to function as an automated Web portal template customization machine-learning training system that: generates a classification feature set via at least one processor-based component of the training system. 13. The automated Web portal template customization machine-learning training system of claim 12 wherein the processor-readable instruction sets further cause the at least one processor to function as an automated Web portal template customization machine-learning training system that: trains an algorithm based at least in part on the generated classification feature set via at least one processor-based component of the training system. 14. The automated Web portal template customization machine-learning training system of claim 13 wherein the processor-readable instruction sets that cause the at least one processor to train a classifier based on the initial positive data set and the initial negative data sets, further causes the at least one processor to: train a decision tree classifier based on the initial positive data set and the initial negative data sets. 15. The automated Web portal template customization machine-learning training system of claim 10 wherein the processor-readable instruction sets further cause the at least one processor to function as an automated Web portal template customization machine-learning training system that: for each of the plurality of Websites of the entities in a first defined field of endeavor, crawls an n-level deep page tree of respective ones of the Websites; and parses a number of Webpages of the respective Websites; and for each of the plurality of Websites of the entities in the or more fields of endeavor that are different from the first defined field of endeavor, crawls an n-level deep page tree of the respective ones of the Websites; and parses a number of Webpages of the respective Websites. 16. The automated Web portal template customization machine-learning training system of claim 15 wherein the processor-readable instruction sets further cause the at least one processor to function as an automated Web portal template customization machine-learning training system that: identifies potential areas of styling in the Webpages that comprise the respective Websites; and extracts cascade style sheet (CSS) information from at least some of the identified potential areas of styling. 17. The automated Web portal template customization machine-learning training system of claim 15 wherein the processor-readable instruction sets that cause the at least one processor to parse a number of Webpages of the respective Web sites further causes the at least one processor to: parse a number of Webpages of the respective Websites; and responsive to parsing the number of Webpages, generate a pair collection of color and corresponding feature vectors, the feature vectors comprising an n-dimensional vector of numerical values for respective attributes associated with the respective color in the respective Web sites.