Search results
Results from the WOW.Com Content Network
OpenCog, a GPL-licensed framework for artificial intelligence written in C++, Python and Scheme. [15] PolyAnalyst: A commercial tool for data mining, text mining, and knowledge management. [89] RapidMiner, an environment for machine learning and data mining, now developed commercially. [90]
The data is collected by crowdsourcing. In a new project OpenSeaMap collect shallow water depths worldwide for making bathimetric charts. OpenSignal is a project to independently map cell phone carrier coverage and performance. All data is collected from a smartphone application that has been downloaded over 3.5m times worldwide.
Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases).
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
MADlib: Scalable, Big Data, SQL-driven machine learning framework for Data Scientists; Mahout: machine learning and data mining solution. Mahout; ManifoldCF: Open-source software for transferring content between repositories or search indexes; Maven: Java project management and comprehension tool
Fraud detection is a knowledge-intensive activity. The main AI techniques used for fraud detection include: . Data mining to classify, cluster, and segment the data and automatically find associations and rules in the data that may signify interesting patterns, including those related to fraud.
A review and critique of data mining process models in 2009 called the CRISP-DM the "de facto standard for developing data mining and knowledge discovery projects." [16] Other reviews of CRISP-DM and data mining process models include Kurgan and Musilek's 2006 review, [8] and Azevedo and Santos' 2008 comparison of CRISP-DM and SEMMA. [9]
Data augmentation; Data exploration; Data preprocessing; Data-driven astronomy; Data-driven model; Decision list; Decision tree pruning; Developmental robotics; Digital signal processing and machine learning; Discovery system (AI research) Document classification; Domain adaptation; Double descent