series called "test data" that we will use to measure the error rate. As a general rule it is important to distinguish between types of errors (false positives and false negatives). For example, in the case on an aggregator of content for children, it doesn't have the same gravity to allow the passage of information not suitable for them, that shows violence or pornography, than the mistake to discard some appropriated information. To improve the system to lower error rates and have these systems with learning capabilities similar to humans we require development of systems that simulate human cognitive abilities, such as natural-language understanding, capturing meaning Common and other forms of advanced processing to achieve the semantics of information. Fields of use Nowadays, there are numerous techniques to develop information filters, some of these reach error rates lower than 10% in various experiments. Among these techniques there are decision trees, support vector machines, neural networks, Bayesian networks, linear discriminants, logistic regression, etc.. At present, these techniques are used in different applications, not only in the web context, but in thematic issues as varied as voice recognition, classification of telescopic astronomy or evaluation of financial risk. See also Information literacy Kalman filter Filter bubble Information explosion Information overload Information society Artificial intelligence References Hanani, U., Shapira, B., Shoval, P. (2001) Information filtering: Overview of issues, research and systems. User Modeling and User-Adapted Interaction, 11, pp. 203–259. http://www.infoworld.com/d/developer-world/human-information-filter-813 External links Infoworld IEEXplore Information systems Social information processing Software by type Filters