Statistical classificationIn statistics, classification is the problem of identifying which of a set of categories (sub-populations) an observation (or observations) belongs to. Examples are assigning a given email to the "spam" or "non-spam" class, and assigning a diagnosis to a given patient based on observed characteristics of the patient (sex, blood pressure, presence or absence of certain symptoms, etc.). Often, the individual observations are analyzed into a set of quantifiable properties, known variously as explanatory variables or features.
Dark skinDark skin is a type of human skin color that is rich in melanin pigments. People with very dark skin are often referred to as "black people", although this usage can be ambiguous in some countries where it is also used to specifically refer to different ethnic groups or populations. The evolution of dark skin is believed to have begun around 1.2 million years ago, in light-skinned early hominid species after they moved from the equatorial rainforest to the sunny savannas.
Human skinThe human skin is the outer covering of the body and is the largest organ of the integumentary system. The skin has up to seven layers of ectodermal tissue guarding muscles, bones, ligaments and internal organs. Human skin is similar to most of the other mammals' skin, and it is very similar to pig skin. Though nearly all human skin is covered with hair follicles, it can appear hairless. There are two general types of skin, hairy and glabrous skin (hairless). The adjective cutaneous literally means "of the skin" (from Latin cutis, skin).
Learning classifier systemLearning classifier systems, or LCS, are a paradigm of rule-based machine learning methods that combine a discovery component (e.g. typically a genetic algorithm) with a learning component (performing either supervised learning, reinforcement learning, or unsupervised learning). Learning classifier systems seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions (e.g. behavior modeling, classification, data mining, regression, function approximation, or game strategy).
Global mental healthGlobal mental health is the international perspective on different aspects of mental health. It is 'the area of study, research and practice that places a priority on improving mental health and achieving equity in mental health for all people worldwide'. There is a growing body of criticism of the global mental health movement, and has been widely criticised as a neo-colonial or "missionary" project and as primarily a front for pharmaceutical companies seeking new clients for psychiatric drugs.
Message Passing InterfaceMessage Passing Interface (MPI) is a standardized and portable message-passing standard designed to function on parallel computing architectures. The MPI standard defines the syntax and semantics of library routines that are useful to a wide range of users writing portable message-passing programs in C, C++, and Fortran. There are several open-source MPI implementations, which fostered the development of a parallel software industry, and encouraged development of portable and scalable large-scale parallel applications.
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.
Multiclass classificationIn machine learning and statistical classification, multiclass classification or multinomial classification is the problem of classifying instances into one of three or more classes (classifying instances into one of two classes is called binary classification). While many classification algorithms (notably multinomial logistic regression) naturally permit the use of more than two classes, some are by nature binary algorithms; these can, however, be turned into multinomial classifiers by a variety of strategies.
Fight-or-flight responseThe fight-or-flight or the fight-flight-or-freeze-fawn (also called hyperarousal or the acute stress response) is a physiological reaction that occurs in response to a perceived harmful event, attack, or threat to survival. It was first described by Walter Bradford Cannon. His theory states that animals react to threats with a general discharge of the sympathetic nervous system, preparing the animal for fighting or fleeing. More specifically, the adrenal medulla produces a hormonal cascade that results in the secretion of catecholamines, especially norepinephrine and epinephrine.
Stress (biology)Stress, either physiological, biological or psychological, is an organism's response to a stressor such as an environmental condition. Stress is the body's method of reacting to a condition such as a threat, challenge or physical and psychological barrier. There are two hormones that an individual produces during a stressful situation, well known as adrenaline and cortisol. There are two kinds of stress hormone levels. Resting (basal) cortisol levels are normal everyday quantities that are essential for standard functioning.
Faster-than-lightFaster-than-light (also FTL, superluminal or supercausal) travel and communication are the conjectural propagation of matter or information faster than the speed of light (c). The special theory of relativity implies that only particles with zero rest mass (i.e., photons) may travel at the speed of light, and that nothing may travel faster. Particles whose speed exceeds that of light (tachyons) have been hypothesized, but their existence would violate causality and would imply time travel.
Ensemble learningIn statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Unlike a statistical ensemble in statistical mechanics, which is usually infinite, a machine learning ensemble consists of only a concrete finite set of alternative models, but typically allows for much more flexible structure to exist among those alternatives.
