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Data-driven models encompass a wide range of techniques and methodologies that aim to intelligently process and analyse large datasets. Examples include fuzzy logic, fuzzy and rough sets for handling uncertainty, [3] neural networks for approximating functions, [4] global optimization and evolutionary computing, [5] statistical learning theory, [6] and Bayesian methods. [7]
The goal of the Hutter Prize is to encourage research in artificial intelligence (AI). The organizers believe that text compression and AI are equivalent problems. Hutter proved that the optimal behavior of a goal-seeking agent in an unknown but computable environment is to guess at each step that the environment is probably controlled by one of the shortest programs consistent with all ...
The Netflix Prize is one such competition. Since then there have been several platforms developed on the idea of data science competitions. Research has been completed on how competition can improve research performance. Companies like JPMorgan Chase also run internal contests involving large numbers of employees. [2]
Data are ordered, timestamped, single-valued metrics. All data files contain anomalies, unless otherwise noted. None 50+ files CSV Anomaly detection: 2016 (continually updated) [328] Numenta Skoltech Anomaly Benchmark (SKAB) Each file represents a single experiment and contains a single anomaly.
NYC BigApps is an annual competition sponsored by the New York City Economic Development Corporation.It provides programmers, developers, designers, and entrepreneurs with access to municipal data sets to build technological products that address civic issues affecting New York City.
Data-informed decision-making (DIDM) gives reference to the collection and analysis of data to guide decisions that improve success. [1] Another form of this process is referred to as data-driven decision-making, "which is defined similarly as making decisions based on hard data as opposed to intuition, observation, or guesswork."
Data-driven learning (DDL) is an approach to foreign language learning. Whereas most language learning is guided by teachers and textbooks, data-driven learning treats language as data and students as researchers undertaking guided discovery tasks. Underpinning this pedagogical approach is the data - information - knowledge paradigm (see DIKW ...
Data analysis focuses on the process of examining past data through business understanding, data understanding, data preparation, modeling and evaluation, and deployment. [8] It is a subset of data analytics, which takes multiple data analysis processes to focus on why an event happened and what may happen in the future based on the previous data.