Stability, also known as algorithmic stability, is a notion in computational learning theory of how a machine learning algorithm output is changed with...

distributions Stability (learning theory), a property of machine learning algorithms Stability, a property of sorting algorithms Numerical stability, a property...

computational learning theory (or just learning theory) is a subfield of artificial intelligence devoted to studying the design and analysis of machine learning algorithms...

Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. Statistical learning theory deals...

activation function. Logistic function Rectifier (neural networks) Stability (learning theory) Softmax function Hinkelmann, Knut. "Neural Networks, p. 7" (PDF)...

brain neurons during the learning process. It was introduced by Donald Hebb in his 1949 book The Organization of Behavior. The theory is also called Hebb's...

'plasticity/stability' problem, i.e. the problem of acquiring new knowledge without disrupting existing knowledge that is also called incremental learning. The...

methods for learning Semantic analysis Similarity learning Sparse dictionary learning Stability (learning theory) Statistical learning theory Statistical...

control system theory, the Routh–Hurwitz stability criterion is a mathematical test that is a necessary and sufficient condition for the stability of a linear...

provide generalization conditions for learning algorithms. From this point of view, VC theory is related to stability, which is an alternative approach for...

contributed to the establishment of control stability criteria; and from 1922 onwards, the development of PID control theory by Nicolas Minorsky. Although a major...

concepts of stability theory to broader contexts, such as simple and NIP theories. A common goal in model theory is to study a first-order theory by analyzing...

the term "Theory of Models" in publication in 1954. Since the 1970s, the subject has been shaped decisively by Saharon Shelah's stability theory. Compared...

learning) Bootstrap aggregating (bagging) Out-of-bag error Bootstrapping (statistics) Leakage (machine learning) Model selection Stability (learning theory)...

biology, game theory has been used as a model to understand many different phenomena. It was first used to explain the evolution (and stability) of the approximate...

For supervised learning applications in machine learning and statistical learning theory, generalization error (also known as the out-of-sample error...

015. Chowdhary, Girish; Johnson, Eric (2011). "Theory and flight-test validation of a concurrent learning adaptive controller". Journal of Guidance, Control...

and Machine Learning. New York: Springer. ISBN 978-0-387-31073-2. Vapnik VN, Vapnik VN (1998). The nature of statistical learning theory (Corrected 2nd...

observational learning include exposure to the model, acquiring the model's behaviour and accepting it as one's own. Bandura's social cognitive learning theory states...

machine learning (ML), boosting is an ensemble metaheuristic for primarily reducing bias (as opposed to variance). It can also improve the stability and accuracy...

rate Portal: Psychology Learning curve Power law Forgetting Power Law [Snoddy, 1926] Snoddy, G. S. (1926). Learning and stability: a psychophysiological...

Algorithmic game theory (AGT) is an area in the intersection of game theory and computer science, with the objective of understanding and design of algorithms...

with inverse reinforcement learning), or the agent can seek information to improve its preferences. Information value theory can be used to weigh the value...

Mastery learning (or, as it was initially called, "learning for mastery"; also known as "mastery-based learning") is an instructional strategy and educational...

pocket algorithm with ratchet (Gallant, 1990) solves the stability problem of perceptron learning by keeping the best solution seen so far "in its pocket"...

Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation...

(2001). Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability. Wiley. ISBN 978-0-471-49517-8. Grossberg, Stephen...

empirical research. The EMH provides the basic logic for modern risk-based theories of asset prices, and frameworks such as consumption-based asset pricing...

Complexity theory also relates to knowledge management (KM) and organizational learning (OL). "Complex systems are, by any other definition, learning organizations...

Multimodal learning is a type of deep learning that integrates and processes multiple types of data, referred to as modalities, such as text, audio, images...