learning+algorithm

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• Learning classifier system — A learning classifier system, or LCS, is a machine learning system with close links to reinforcement learning and genetic algorithms. First described by John Holland, his LCS consisted of a population of binary rules on which a genetic algorithm… …   Wikipedia

• Algorithm — Flow chart of an algorithm (Euclid s algorithm) for calculating the greatest common divisor (g.c.d.) of two numbers a and b in locations named A and B. The algorithm proceeds by successive subtractions in two loops: IF the test B ≤ A yields yes… …   Wikipedia

• Algorithm characterizations — The word algorithm does not have a generally accepted definition. Researchers are actively working in formalizing this term. This article will present some of the characterizations of the notion of algorithm in more detail. This article is a… …   Wikipedia

• One-shot learning — is an object categorization problem of current research interest in computer vision. Whereas most machine learning based object categorization algorithms require training on hundreds or thousands of images and very large datasets, one shot… …   Wikipedia

• Memetic algorithm — Memetic algorithms (MA) represent one of the recent growing areas of research in evolutionary computation. The term MA is now widely used as a synergy of evolutionary or any population based approach with separate individual learning or local… …   Wikipedia

• Meta learning (computer science) — This article is about meta learning in computer science. For meta learning in social psychology, see Meta learning. Meta learning is a subfield of Machine learning where automatic learning algorithms are applied on meta data about machine… …   Wikipedia

• Online machine learning — In machine learning, online learning is a model of induction that learns one instance at a time. The goal in online learning is to predict labels for instances. For example, the instances could describe the current conditions of the stock market …   Wikipedia

• Machine learning — is a subfield of artificial intelligence that is concerned with the design and development of algorithms and techniques that allow computers to learn . In general, there are two types of learning: inductive, and deductive. Inductive machine… …   Wikipedia

• Constraint learning — In constraint satisfaction backtracking algorithms, constraint learning is a technique for improving efficiency. It works by recording new constraints whenever an inconsistency is found. This new constraint may reduce the search space, as future… …   Wikipedia

• Supervised learning — is a machine learning technique for learning a function from training data. The training data consist of pairs of input objects (typically vectors), and desired outputs. The output of the functioncan be a continuous value (called regression), or… …   Wikipedia

• Temporal difference learning — is a prediction method. It has been mostly used for solving the reinforcement learning problem. TD learning is a combination of Monte Carlo ideas and dynamic programming (DP) ideas. [2] TD resembles a Monte Carlo method because it learns by… …   Wikipedia