Machine learningMachine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches.
Online machine learningIn computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update the best predictor for future data at each step, as opposed to batch learning techniques which generate the best predictor by learning on the entire training data set at once. Online learning is a common technique used in areas of machine learning where it is computationally infeasible to train over the entire dataset, requiring the need of out-of-core algorithms.
LearningLearning is the process of acquiring new understanding, knowledge, behaviors, skills, values, attitudes, and preferences. The ability to learn is possessed by humans, animals, and some machines; there is also evidence for some kind of learning in certain plants. Some learning is immediate, induced by a single event (e.g. being burned by a hot stove), but much skill and knowledge accumulate from repeated experiences. The changes induced by learning often last a lifetime, and it is hard to distinguish learned material that seems to be "lost" from that which cannot be retrieved.
Balance (ability)Balance in biomechanics, is an ability to maintain the line of gravity (vertical line from centre of mass) of a body within the base of support with minimal postural sway. Sway is the horizontal movement of the centre of gravity even when a person is standing still. A certain amount of sway is essential and inevitable due to small perturbations within the body (e.g., breathing, shifting body weight from one foot to the other or from forefoot to rearfoot) or from external triggers (e.g., visual distortions, floor translations).
Sensory processing disorderSensory processing disorder (SPD, formerly known as sensory integration dysfunction) is a condition in which multisensory input is not adequately processed in order to provide appropriate responses to the demands of the environment. Sensory processing disorder is present in many people with dyspraxia, autism spectrum disorder and attention deficit hyperactivity disorder. Individuals with SPD may inadequately process visual, auditory, olfactory (smell), gustatory (taste), tactile (touch), vestibular (balance), proprioception (body awareness), and interoception (internal body senses) sensory stimuli.
Balance of paymentsIn international economics, the balance of payments (also known as balance of international payments and abbreviated BOP or BoP) of a country is the difference between all money flowing into the country in a particular period of time (e.g., a quarter or a year) and the outflow of money to the rest of the world. These financial transactions are made by individuals, firms and government bodies to compare receipts and payments arising out of trade of goods and services.
Sensory processingSensory processing is the process that organizes and distinguishes sensation (sensory information) from one's own body and the environment, thus making it possible to use the body effectively within the environment. Specifically, it deals with how the brain processes multiple sensory modality inputs, such as proprioception, vision, auditory system, tactile, olfactory, vestibular system, interoception, and taste into usable functional outputs. It has been believed for some time that inputs from different sensory organs are processed in different areas in the brain.
Balance of tradeThe balance of trade, commercial balance, or net exports (sometimes symbolized as NX), is the difference between the monetary value of a nation's exports and imports over a certain time period. Sometimes a distinction is made between a balance of trade for goods versus one for services. The balance of trade measures a flow of exports and imports over a given period of time. The notion of the balance of trade does not mean that exports and imports are "in balance" with each other.
Supervised learningSupervised learning (SL) is a paradigm in machine learning where input objects (for example, a vector of predictor variables) and a desired output value (also known as human-labeled supervisory signal) train a model. The training data is processed, building a function that maps new data on expected output values. An optimal scenario will allow for the algorithm to correctly determine output values for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way (see inductive bias).
Balance disorderA balance disorder is a disturbance that causes an individual to feel unsteady, for example when standing or walking. It may be accompanied by feelings of giddiness, or wooziness, or having a sensation of movement, spinning, or floating. Balance is the result of several body systems working together: the visual system (eyes), vestibular system (ears) and proprioception (the body's sense of where it is in space). Degeneration or loss of function in any of these systems can lead to balance deficits.
Government budget balanceThe government budget balance, also referred to as the general government balance, public budget balance, or public fiscal balance, is the difference between government revenues and spending. For a government that uses accrual accounting (rather than cash accounting) the budget balance is calculated using only spending on current operations, with expenditure on new capital assets excluded. A positive balance is called a government budget surplus, and a negative balance is a government budget deficit.
Rule-based machine learningRule-based machine learning (RBML) is a term in computer science intended to encompass any machine learning method that identifies, learns, or evolves 'rules' to store, manipulate or apply. The defining characteristic of a rule-based machine learner is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learners that commonly identify a singular model that can be universally applied to any instance in order to make a prediction.
