Feature (machine learning)In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a data set. Choosing informative, discriminating, and independent features is crucial to producing effective algorithms for pattern recognition, classification, and regression tasks. Features are usually numeric, but other types such as strings and graphs are used in syntactic pattern recognition, after some pre-processing step such as one-hot encoding.
Symbolic artificial intelligenceMethods in artificial intelligence researchIn artificial intelligence (AI), symbolic artificial intelligence (also known as classical artificial intelligence or logic-based artificial intelligence) is a collection of methods based on high-level symbolic (human-readable) representations of problems, logic, and search. Symbolic AI used tools such as logic programming, production rules, semantic nets and frames.Binary classificationBinary classification is the task of putting things into one of two categories (each called a class). As such, it is the simplest form of the general task of classification into any number of classes.
HypothesisA hypothesis (pl.: hypotheses) is a proposed explanation for a phenomenon. A scientific hypothesis must be based on observations and make a testable and reproducible prediction about reality, in a process beginning with an educated guess. If a hypothesis is repeatedly independently demonstrated by experiment to be true, it becomes a scientific theory.Machine learningMachine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without being explicitly programmed. Statistics and mathematical optimisation methods compose the foundations of machine learning. Data mining is a related field of study, focusing on exploratory data analysis (EDA) through unsupervised learning.Algorithmic learning theoryAlgorithmic learning theory is a mathematical framework for analyzing machine learning problems and algorithms. Synonyms include formal learning theory and algorithmic inductive inference. Algorithmic learning theory is different from statistical learning theory in that it does not make use of statistical assumptions and analysis.
Inductive logic programmingInductive logic programming (ILP) is a subfield of symbolic artificial intelligence which uses logic programming as a uniform representation for examples, background knowledge and hypotheses. The term "inductive" here refers to philosophical (i.e. suggesting a theory to explain observed facts) rather than mathematical (i.e. proving a property for all members of a well-ordered set) induction.
Logical disjunctionRelated conceptsPropositional calculusPredicate logicBoolean algebraTruth tableTruth functionBoolean functionFunctional completenessScope (logic) ApplicationsDigital logicProgramming languagesMathematical logicPhilosophy of logicIn logic, disjunction (also known as logical disjunction, logical or, logical addition, or inclusive disjunction) is a logical connective typically notated as ∨ and read aloud as "or".
ConsistencyIn deductive logic, a consistenttheory is one that does not lead to a logical contradiction. A theory T{\displaystyle T} is consistent if there is no formulaφ{\displaystyle \varphi } such that both φ{\displaystyle \varphi } and its negation ¬φ{\displaystyle \lnot \varphi } are elements of the set of consequences of T{\displaystyle T}.
State-space searchState-space search is a process used in the field of computer science, including artificial intelligence (AI), in which successive configurations or states of an instance are considered, with the intention of finding a goal state with the desired property. Problems are often modelled as a state space, a set of states that a problem can be in.Inductive biasThe inductive bias (also known as learning bias) of a learning algorithm is the set of assumptions that the learner uses to predict outputs of given inputs that it has not encountered. Inductive bias is anything which makes the algorithm learn one pattern instead of another pattern (e.g., step-functions in decision trees instead of continuous functions in linear regression models).
Sentence (mathematical logic)In mathematical logic, a sentence (or closed formula) of a predicate logic is a Boolean-valued well-formed formula with no free variables. A sentence can be viewed as expressing a proposition, something that must be true or false. The restriction of having no free variables is needed to make sure that sentences can have concrete, fixed truth values: as the free variables of a (general) formula can range over several values, the truth value of such a formula may vary.*As an Amazon Associate I earn from qualifying purchases.