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- этимология
Что (кто) такое complete inference system - определение
COMPONENT OF THE SYSTEM THAT APPLIES LOGICAL RULES TO THE KNOWLEDGE BASE TO DEDUCE NEW INFORMATION
Expert system shell; Inference system; Rule-based inference engine
Найдено результатов: 10659
Statistical inference
PROCESS OF DEDUCING PROPERTIES OF AN UNDERLYING PROBABILITY DISTRIBUTION BY ANALYSIS OF DATA
InterpretingStatisticalData; Interpreting statistical data; Inferential statistics; Statistical analysis; Non-parametric inference; Inferential Statistics; Inductive strength; Inductive statistics; Statistical induction; Predictive inference; Statistics/Inference; Interpreting Statistical Data; Statistical Inference; Sampling statistics; Prediction theory; Inference (machine learning)
Statistical inference is the process of using data analysis to infer properties of an underlying distribution of probability.Upton, G.
complete graph
SIMPLE UNDIRECTED GRAPH IN WHICH EVERY PAIR OF DISTINCT VERTICES IS CONNECTED BY A UNIQUE EDGE
Full graph; Complete Digraph; Complete digraph; K n; Tetrahedral Graph; Complete graphs
A graph which has a link between every pair of nodes. A
complete bipartite graph can be partitioned into two subsets
of nodes such that each node is joined to every node in the
other subset.
(1995-01-24)
Complete (complexity)
NOTION OF THE "HARDEST" OR "MOST GENERAL" PROBLEM IN A COMPLEXITY CLASS
Complete problem; Hard (complexity)
In computational complexity theory, a computational problem is complete for a complexity class if it is, in a technical sense, among the "hardest" (or "most expressive") problems in the complexity class.
inference engine
A program that infers new facts from known facts using
inference rules. Commonly found as part of a Prolog
interpreter, expert system or knowledge based system.
(1994-11-01)
Inference engine
In the field of artificial intelligence, an inference engine is a component of the system that applies logical rules to the knowledge base to deduce new information. The first inference engines were components of expert systems.
♯P-complete
COMPLEXITY CLASS
Sharp-P-Complete; Sharp P complete; Number-P hard; Number-P-complete; Sharp-P hard; Sharp-P-complete
The #P-complete problems (pronounced "sharp P complete" or "number P complete") form a complexity class in computational complexity theory. The problems in this complexity class are defined by having the following two properties:
Adaptive neuro fuzzy inference system
Anfis; Adaptive Neuro Fuzzy Inference System; ANFIS; Adaptive-network-based fuzzy inference system; Adaptive network-based fuzzy inference system; Adaptive neuro-fuzzy inference system; Adaptive-neuro-fuzzy inference system
An adaptive neuro-fuzzy inference system or adaptive network-based fuzzy inference system (ANFIS) is a kind of artificial neural network that is based on Takagi–Sugeno fuzzy inference system. The technique was developed in the early 1990s.
Chain-complete partial order
POSET COMPLETION
Chain complete; Chain completeness
In mathematics, specifically order theory, a partially ordered set is chain-complete if every chain in it has a least upper bound. It is ω-complete when every increasing sequence of elements (a type of countable chain) has a least upper bound; the same notion can be extended to other cardinalities of chains..
NP-complete
![Levin]] proved that each easy-to-verify problem can be solved as fast as SAT, which is hence NP-complete.](https://commons.wikimedia.org/wiki/Special:FilePath/3SAT 17 svg.svg?width=200 "Levin]] proved that each easy-to-verify problem can be solved as fast as SAT, which is hence NP-complete.")
![P≠NP]], while the right side is valid under the assumption that P=NP (except that the empty language and its complement are never NP-complete, and in general, not every problem in P or NP is NP-complete)](https://commons.wikimedia.org/wiki/Special:FilePath/P np np-complete np-hard.svg?width=200 "P≠NP]], while the right side is valid under the assumption that P=NP (except that the empty language and its complement are never NP-complete, and in general, not every problem in P or NP is NP-complete)")
![reductions]] typically used to prove their NP-completeness](https://commons.wikimedia.org/wiki/Special:FilePath/Relative NPC chart.svg?width=200 "reductions]] typically used to prove their NP-completeness")
COMPLEXITY CLASS
NP-complete problem; NP-complete problems; NP complete; NP completeness; NP-C; Np complete; Np-complete; NP-complete language; Np-complete problem; NP-Completeness; Np completeness; Non-deterministic polynomial-time complete; NP-Complete; Nondeterministic Polynomial Complete; Non polynomial complete; Np-Complete; NP-complete; NP-incomplete
<complexity> (NPC, Nondeterministic Polynomial time complete)
A set or property of computational decision problems which
is a subset of NP (i.e. can be solved by a
nondeterministic Turing Machine in polynomial time),
with the additional property that it is also NP-hard. Thus
a solution for one NP-complete problem would solve all
problems in NP. Many (but not all) naturally arising problems
in class NP are in fact NP-complete.
There is always a polynomial-time algorithm for transforming
an instance of any NP-complete problem into an instance of any
other NP-complete problem. So if you could solve one you
could solve any other by transforming it to the solved one.
The first problem ever shown to be NP-complete was the
satisfiability problem. Another example is {Hamilton's
problem}.
See also computational complexity, halting problem,
Co-NP, NP-hard.
http://fi-www.arc.nasa.gov/fia/projects/bayes-group/group/NP/.
[Other examples? ]
(1995-04-10)
Википедия
Inference engine
In the field of artificial intelligence, an inference engine is a component of the system that applies logical rules to the knowledge base to deduce new information. The first inference engines were components of expert systems. The typical expert system consisted of a knowledge base and an inference engine. The knowledge base stored facts about the world. The inference engine applies logical rules to the knowledge base and deduced new knowledge. This process would iterate as each new fact in the knowledge base could trigger additional rules in the inference engine. Inference engines work primarily in one of two modes either special rule or facts: forward chaining and backward chaining. Forward chaining starts with the known facts and asserts new facts. Backward chaining starts with goals, and works backward to determine what facts must be asserted so that the goals can be achieved.
