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Concept Glossary: K

This page lists English glossary entries for this letter. Entry bodies are assembled from term source files.

kernel

  • Meaning: A core function that takes two inputs and computes how similar or close they are in a richer representation space. In boundary-based classification, it is read as the idea of getting the comparison effect of a new representation space through calculations in the original space, without explicitly building every new feature.
  • Why it matters: When a linear boundary feels awkward in the original coordinate space, the problem may be the representation space rather than the boundary alone. The kernel helps explain nonlinear structure as changing the space used to read the data, not merely drawing a more complicated line.
  • Related concepts: representation, distance, decision boundary, dimensionality reduction
  • Core Section: P4-13.2
  • Appears in: P4-13.2

k-means

  • Meaning: k-means is a center-based clustering method that divides data points into a preset number of clusters and repeatedly reduces the distance between each point and the centroid of its assigned cluster.
  • Why it matters: k-means gives a simple first intuition for unsupervised learning: grouping similar cases without answer labels. It also has clear limits, because the number of clusters must be chosen first and the result is sensitive to distance and centroid assumptions.
  • Related concepts: clustering, cluster, centroid, distance
  • Core Section: P4-17.1
  • Appears in: P4-17.3

knowledge base

  • Meaning: A knowledge base is a structured store of rules, facts, relationships, and domain information that a system can consult. It separates knowledge materials from the procedure that applies them.
  • Why it matters: The concept helps readers see why rule-based systems are more than long lists of conditional code. The knowledge base stores materials, while an inference process uses them.
  • Related concepts: rule-based system, knowledge representation, inference engine
  • Core Section: P1-3.1
  • Appears in: P1-2.1, P1-3.3

knowledge representation

  • Meaning: Knowledge representation is the work of organizing facts, relationships, rules, and constraints in a form that a computer can inspect, compare, and reason over.
  • Why it matters: Symbolic AI depends on not only what knowledge exists, but how that knowledge is written. Tables, rules, graphs, and logical forms support different questions and reasoning procedures.
  • Related concepts: symbolic AI, rule-based system
  • Core Section: P1-2.1
  • Appears in: P1-2.2

  • Meaning: A search method that finds documents or passages by matching explicit words or terms in the query.
  • Why it matters: It gives a useful contrast to similarity search, where related meaning can be found even without exact word overlap.
  • Related concepts: Read together with the surrounding glossary entries and the linked Sections for this term.
  • Core Section: P1-13.2