In machine learning, instance-based learning (sometimes called memory-based learning) is a family of learning algorithms that, instead of performing explicit...

Broadbent's Sugar Production Factory task[clarification needed]. The Instance-Based Learning Theory (IBLT) is a theory of how humans make decisions in dynamic...

In machine learning, multiple-instance learning (MIL) is a type of supervised learning. Instead of receiving a set of instances which are individually...

pre-processing step that can be applied in many machine learning (or data mining) tasks. Approaches for instance selection can be applied for reducing the original...

encompasses several domains including learning theory, computer-based training, online learning, and m-learning where mobile technologies are used. The...

Image-based lighting, an image rendering technique Inbred backcross lines, a breeding technique InBound Links, a metric used by search engines Instance-based...

handling (GMDH) Inductive logic programming Instance-based learning Lazy learning Learning Automata Learning Vector Quantization Logistic Model Tree Minimum...

k-NN technique, which is instance-based and function is only estimated locally. Theoretical disadvantages with lazy learning include: The large space...

of distance learning. This is the first known instance of the use of materials for independent language study. The concept of e-learning began developing...

task-based learning processes. According to Jon Larsson, in considering problem-based learning for language learning, i.e., task-based language learning:...

In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences. It involves...

learning algorithms that commonly identify a singular model that can be universally applied to any instance in order to make a prediction. Rule-based...

"Geometric proximity graphs for improving nearest neighbor methods in instance-based learning and data mining". International Journal of Computational Geometry...

parsed into sub-types. For instance, declarative memory comprises both episodic and semantic memory. Non-associative learning refers to "a relatively permanent...

Rote learning is a memorization technique based on repetition. The method rests on the premise that the recall of repeated material becomes faster the...

value of k leads to high bias and low variance (see below). In instance-based learning, regularization can be achieved varying the mixture of prototypes...

machine learning, supervised learning (SL) is a type of machine learning paradigm where an algorithm learns to map input data to a specific output based on...

seen as low-quality models for that purpose. Most modern deep learning models are based on multi-layered neural networks such as convolutional neural...

problem solving. Game-based learning (GBL) is a type of game play that has defined learning outcomes. Generally, game-based learning is designed to balance...

(model-based) learning effective distance metrics (metrics-based) explicitly optimizing model parameters for fast learning (optimization-based). Model-based...

Content-based image retrieval Curse of dimensionality Digital signal processing Dimension reduction Fixed-radius near neighbors Fourier analysis Instance-based...

included meander (art), compass and straightedge constructions, instance-based learning, music information retrieval, and computational music theory. He...

as materials science. For instance, graph neural networks (GNNs) have demonstrated their capability in scaling deep learning for the discovery of new stable...

An Instance of the Fingerpost is a 1997 historical mystery novel by Iain Pears. The main setting is Oxford in 1663, with the events initially revolving...

Explanation-based learning (EBL) is a form of machine learning that exploits a very strong, or even perfect, domain theory (i.e. a formal theory of an...

in learning, decisions are made based on properties alone and rely on simple criteria that do not require a lot of memory. Example of rule-based theory:...

Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs...

datapoint. As contrasted with Pool-based sampling, the obvious drawback of stream-based methods is that the learning algorithm does not have sufficient...

Jinyan; et al. (2004). "Deeps: A new instance-based lazy discovery and classification system". Machine Learning. 54 (2): 99–124. doi:10.1023/b:mach.0000011804...

psychologists have argued that this "is not an instance of learning styles, rather, it is an instance of ability appearing as a style". Likewise, Fleming...