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In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a data set. [1] Choosing informative, discriminating, and independent features is crucial to produce effective algorithms for pattern recognition, classification, and regression tasks.
It has many of the features of Lisp Machine Lisp (a large Lisp dialect used to program Lisp Machines), but was designed to be efficiently implementable on any personal computer or workstation. Common Lisp is a general-purpose programming language and thus has a large language standard including many built-in data types, functions, macros and ...
All operating system software was written in Lisp. Xerox used Interlisp. Symbolics, LMI, and TI used Lisp Machine Lisp (descendant of MacLisp). With the appearance of Common Lisp, Common Lisp was supported on the Lisp Machines and some system software was ported to Common Lisp or later written in Common Lisp.
Lisp Machines, Inc. was a company formed in 1979 by Richard Greenblatt of MIT's Artificial Intelligence Laboratory to build Lisp machines. It was based in Cambridge, Massachusetts . By 1979, the Lisp Machine Project at MIT, originated and headed by Greenblatt, had constructed over 30 CADR computers for various projects at MIT.
Lisp Machine Lisp is a programming language, a dialect of the language Lisp. A direct descendant of Maclisp , it was initially developed in the mid to late 1970s as the system programming language for the Massachusetts Institute of Technology (MIT) Lisp machines .
Lisp Machine Lisp: 1984: Sometimes named Zetalisp, is a direct descendant of Maclisp; was developed in the mid to late 1970s as the systems programming language for the MIT Lisp machines [23] Lispkit Lisp: 1980: Peter Henderson: A lexically scoped, purely functional subset of Lisp ("Pure Lisp") developed as a testbed for functional programming ...
A Symbolics 3640 Lisp machine: an early (1984) platform for expert systems. In artificial intelligence (AI), an expert system is a computer system emulating the decision-making ability of a human expert. [1]
Feature engineering in machine learning and statistical modeling involves selecting, creating, transforming, and extracting data features. Key components include feature creation from existing data, transforming and imputing missing or invalid features, reducing data dimensionality through methods like Principal Components Analysis (PCA), Independent Component Analysis (ICA), and Linear ...