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The input–process–output model. The input–process–output (IPO) model, or input-process-output pattern, is a widely used approach in systems analysis and software engineering for describing the structure of an information processing program or other process.
The Delphi method or Delphi technique (/ ˈ d ɛ l f aɪ / DEL-fy; also known as Estimate-Talk-Estimate or ETE) is a structured communication technique or method, originally developed as a systematic, interactive forecasting method that relies on a panel of experts.
This can be seen as a structured prediction problem [2] in which the structured output domain is the set of all possible parse trees. Structured prediction is used in a wide variety of domains including bioinformatics , natural language processing (NLP), speech recognition , and computer vision .
Probabilistic Soft Logic (PSL) is a statistical relational learning (SRL) framework for modeling probabilistic and relational domains. [2] It is applicable to a variety of machine learning problems, such as collective classification, entity resolution, link prediction, and ontology alignment.
Branch prediction attempts to guess whether a conditional jump will be taken or not. Branch target prediction attempts to guess the target of a taken conditional or unconditional jump before it is computed by decoding and executing the instruction itself. Branch prediction and branch target prediction are often combined into the same circuitry.
PPLs often extend from a basic language. For instance, Turing.jl [12] is based on Julia, Infer.NET is based on .NET Framework, [13] while PRISM extends from Prolog. [14] However, some PPLs, such as WinBUGS, offer a self-contained language that maps closely to the mathematical representation of the statistical models, with no obvious origin in another programming language.
TestingCup – Polish Championship in Software Testing, Katowice, May 2016 Software testing is the act of checking whether software satisfies expectations.. Software testing can provide objective, independent information about the quality of software and the risk of its failure to a user or sponsor.
Semantic data mining is a subset of data mining that specifically seeks to incorporate domain knowledge, such as formal semantics, into the data mining process.Domain knowledge is the knowledge of the environment the data was processed in. Domain knowledge can have a positive influence on many aspects of data mining, such as filtering out redundant or inconsistent data during the preprocessing ...