The phrase "adaptation learning" is correct and usable in written English.
It can be used in contexts related to education, training, or cognitive science, particularly when discussing how individuals or systems adjust their learning processes based on experiences or changes in their environment.
Example: "The course focuses on adaptation learning, helping students develop skills to modify their approaches based on feedback and new information."
Alternatives: "adaptive learning" or "learning adaptation".
Our machines aren't minds yet, but they are taking on more and more of the attributes we used to think of as uniquely human: reason, action, reaction, language, logic, adaptation, learning.
Ubiquitous contextual information, more accurate context representation, and higher order functions (such as adaptation, learning, etc) made possible.
The final propositions present "Adaptation learning process pre-steps framework" that emerges from the description of contrasts in perceptions/cognitions observed between the two study cases.
The symbolic approach utilizes symbolic emotional rule-based systems (rule base that generated emotions) with continuous interactions with environment and an internal "thinking" machinery that comes as a result of series of inferences, evaluation, evolution processes, adaptation, learning, and emotions.
The Intergovernmental Panel on Climate Change (IPCC) defined resilience as the capacity of social, economic, and environmental systems to cope with a hazardous event or trend or disturbance, responding or reorganizing in ways that maintain their essential function, identity, and structure, while also maintaining the capacity for adaptation, learning, and transformation (IPCC 2014).
Ubiquitous contextual information, more accurate context representation, and higher order functions (such as adaptation, learning, etc) made possible Multi-modal intuitive HCI (i.e. based on natural language, gestures, whole body movements, even emotions) paving the way to direct brain to computer interfaces.
The principal result was a perceptron architecture that, through adaptation, learns to ask a specific space time question answered by a subset of the space time binary codes that it can observe.
This enables us to continuously evaluate the accuracy of previous adaptations, learn new adaptation rules based on executable run-time models, and verify the correctness of the adaptation logic in the current system context.
Adaptation means learning to live with these changes, and preparing for other changes that are unavoidable.
What we need is a cultural climate that favours trans-disciplinary thinking and a better appreciation of evolution, adaptation and learning across generations.
Yet, neural mechanisms are crucial in modelling adaptation and learning.
