AI Glossary & Dictionary for “K”
Find the Flux+Form AI glossary & dictionary to help you make sense of common AI terms. Below you can find a AI Glossary & Dictionary for “K”:
Kalman Filter — A Kalman filter estimates true values in noisy systems by combining predictions with measurements. An example might be tracking a ship’s position in stormy weather – it combines GPS readings with knowledge of the ship’s speed and direction to make better estimates.
Kernel Function — A kernel function transforms data into a higher-dimensional space where it might be easier to separate or analyze. Imagine looking at a 2D shadow from different angles until you find the view that best shows the 3D object’s true shape.
Kernel Method — Kernel methods use kernel functions to perform complex analyses without explicitly computing high-dimensional transformations. It’s like solving a puzzle by looking at its reflection in a curved mirror – you can find solutions in transformed spaces without actually creating those transformations.
Kernel PCA — Kernel PCA applies principal component analysis using kernel functions to find nonlinear patterns in data. An example would be having a special lens that helps you see hidden patterns in complicated data – it reveals relationships that aren’t visible in the original form.
Kernel Regression — Kernel regression estimates relationships between variables using weighted averages of nearby observations. Think of it like predicting house prices by looking at sales of similar nearby houses, giving more weight to houses that are more similar.
Kernel Trick — The kernel trick efficiently computes similarity in high-dimensional spaces without explicitly transforming the data. Imagine being able to solve a 3D puzzle while only looking at its 2D shadow – you get the benefits of working in higher dimensions without the computational cost.
Key-Value Store — A key-value store is a database that pairs unique keys with their associated values for quick retrieval. Envision a coat check system where each coat (value) is assigned a unique ticket number (key) for easy retrieval.
K-Fold Cross Validation — K-fold cross validation divides data into k subsets, using each subset as a test set while training on the others. Think of it like testing a recipe k different times, each time holding back a different portion of ingredients to verify the recipe works consistently.
K-Means Clustering — K-means clustering groups data points into k clusters based on similarity. Similar to organizing books on shelves – you decide on k shelves and then put similar books together, with each book going to the shelf with the most similar books.
K-Nearest Neighbors — K-nearest neighbors classifies items based on the majority class of their k closest neighbors in the feature space. This would be like asking your k closest friends for restaurant recommendations – you make decisions based on what similar examples suggest.
Knowledge Base — In AI, a knowledge base is a structured collection of information used by AI systems for reasoning and decision making. Think of it like having a comprehensive reference library that an AI can consult to make informed decisions.
Knowledge Distillation — Knowledge distillation in AI transfers knowledge from a large complex model to a smaller simpler one. Similar to having an expert teacher create a simplified version of their knowledge that’s easier for students to understand and use.
Knowledge Engineering — Knowledge engineering involves capturing and encoding expert knowledge into AI systems. Imagine interviewing master chefs to create a cooking AI – you’re converting human expertise into rules and patterns a computer can understand.
Knowledge Graph — In AI, a knowledge graph represents information as a network of connected entities and relationships. It’s essentially a giant mind map showing how different concepts, people, and things are related to each other.
Knowledge Representation — Knowledge representation defines how information is stored and structured in AI systems. It’s the equivalent of organizing a vast library – you need a systematic way to store information so it can be easily found and used.
Knowledge Transfer — Knowledge transfer applies learning from one task or domain to help with another. An example might be using your experience playing piano to help learn the organ – skills from one area can speed up learning in related areas.
Kullback-Leibler Divergence — Kullback-Leibler divergence measures how one probability distribution differs from another. It would be similar to comparing two different recipes for the same dish – it quantifies how different they are from each other.
This concludes the AI Glossary & Dictionary for “K”
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