Decision tree learningDecision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or regression decision tree is used as a predictive model to draw conclusions about a set of observations. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels.
The Open Source DefinitionThe Open Source Definition is a document published by the Open Source Initiative, to determine whether a software license can be labeled with the open-source certification mark. The definition was taken from the exact text of the Debian Free Software Guidelines, written and adapted primarily by Bruce Perens with input from the Debian developers on a private Debian mailing list. The document was created 9 months before the formation of the Open Source Initiative. Open source doesn't just mean access to the source code.
Comparison of free and open-source software licensesThis comparison only covers software licenses which have a linked Wikipedia article for details and which are approved by at least one of the following expert groups: the Free Software Foundation, the Open Source Initiative, the Debian Project and the Fedora Project. For a list of licenses not specifically intended for software, see List of free-content licences. FOSS stands for "Free and Open Source Software". There is no one universally agreed-upon definition of FOSS software and various groups maintain approved lists of licenses.
History of free and open-source softwareIn the 1950s and 1960s, computer operating software and compilers were delivered as a part of hardware purchases without separate fees. At the time, source code, the human-readable form of software, was generally distributed with the software providing the ability to fix bugs or add new functions. Universities were early adopters of computing technology. Many of the modifications developed by universities were openly shared, in keeping with the academic principles of sharing knowledge, and organizations sprung up to facilitate sharing.
Behavioural sciencesThe behavioural sciences explore the cognitive processes within organisms and the behavioural interactions between organisms in the natural world. It involves the systematic analysis and investigation of human and animal behaviour through naturalistic observation, controlled scientific experimentation and mathematical modeling. It attempts to accomplish legitimate, objective conclusions through rigorous formulations and observation. Examples of behavioural sciences include psychology, psychobiology, anthropology, economics, and cognitive science.
Animal cognitionAnimal cognition encompasses the mental capacities of non-human animals including insect cognition. The study of animal conditioning and learning used in this field was developed from comparative psychology. It has also been strongly influenced by research in ethology, behavioral ecology, and evolutionary psychology; the alternative name cognitive ethology is sometimes used. Many behaviors associated with the term animal intelligence are also subsumed within animal cognition.
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.
Information theoryInformation theory is the mathematical study of the quantification, storage, and communication of information. The field was originally established by the works of Harry Nyquist and Ralph Hartley, in the 1920s, and Claude Shannon in the 1940s. The field, in applied mathematics, is at the intersection of probability theory, statistics, computer science, statistical mechanics, information engineering, and electrical engineering. A key measure in information theory is entropy.
Medical classificationA medical classification is used to transform descriptions of medical diagnoses or procedures into standardized statistical code in a process known as clinical coding. Diagnosis classifications list diagnosis codes, which are used to track diseases and other health conditions, inclusive of chronic diseases such as diabetes mellitus and heart disease, and infectious diseases such as norovirus, the flu, and athlete's foot. Procedure classifications list procedure code, which are used to capture interventional data.
Working animalA working animal is an animal, usually domesticated, that is kept by humans and trained to perform tasks instead of being slaughtered to harvest animal products. Some are used for their physical strength (e.g. oxen and draft horses) or for transportation (e.g. riding horses and camels), while others are service animals trained to execute certain specialized tasks (e.g. hunting and guide dogs, messenger pigeons and fishing cormorants). They may also be used for milking or herding.
Active learning (machine learning)Active learning is a special case of machine learning in which a learning algorithm can interactively query a user (or some other information source) to label new data points with the desired outputs. In statistics literature, it is sometimes also called optimal experimental design. The information source is also called teacher or oracle. There are situations in which unlabeled data is abundant but manual labeling is expensive. In such a scenario, learning algorithms can actively query the user/teacher for labels.
Automated machine learningAutomated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. AutoML potentially includes every stage from beginning with a raw dataset to building a machine learning model ready for deployment. AutoML was proposed as an artificial intelligence-based solution to the growing challenge of applying machine learning. The high degree of automation in AutoML aims to allow non-experts to make use of machine learning models and techniques without requiring them to become experts in machine learning.
