Document classificationDocument classification or document categorization is a problem in library science, information science and computer science. The task is to assign a document to one or more classes or categories. This may be done "manually" (or "intellectually") or algorithmically. The intellectual classification of documents has mostly been the province of library science, while the algorithmic classification of documents is mainly in information science and computer science.
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).
Correlation clusteringClustering is the problem of partitioning data points into groups based on their similarity. Correlation clustering provides a method for clustering a set of objects into the optimum number of clusters without specifying that number in advance. Cluster analysis In machine learning, correlation clustering or cluster editing operates in a scenario where the relationships between the objects are known instead of the actual representations of the objects.
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.
Bayesian inferenceBayesian inference (ˈbeɪziən or ˈbeɪʒən ) is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. Bayesian inference is an important technique in statistics, and especially in mathematical statistics. Bayesian updating is particularly important in the dynamic analysis of a sequence of data. Bayesian inference has found application in a wide range of activities, including science, engineering, philosophy, medicine, sport, and law.
Somatic nervous systemThe somatic nervous system (SNS), or voluntary nervous system is the part of the peripheral nervous system associated with the voluntary control of body movements via skeletal muscles. The somatic nervous system consists of nerves carrying afferent nerve fibers, which relay sensation from the body to the central nervous system (CNS), and nerves carrying efferent nerve fibers, which relay motor commands from the CNS to stimulate muscle contraction. The a- of afferent and the e- of efferent correspond to the prefixes ad- (to, toward) and ex- (out of).
Prefrontal cortexIn mammalian brain anatomy, the prefrontal cortex (PFC) covers the front part of the frontal lobe of the cerebral cortex. The PFC contains the Brodmann areas BA8, BA9, BA10, BA11, BA12, BA13, BA14, BA24, BA25, BA32, BA44, BA45, BA46, and BA47. The basic activity of this brain region is considered to be orchestration of thoughts and actions in accordance with internal goals. Many authors have indicated an integral link between a person's will to live, personality, and the functions of the prefrontal cortex.
PerceptronIn machine learning, the perceptron (or McCulloch-Pitts neuron) is an algorithm for supervised learning of binary classifiers. A binary classifier is a function which can decide whether or not an input, represented by a vector of numbers, belongs to some specific class. It is a type of linear classifier, i.e. a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector.
Pseudounipolar neuronA pseudounipolar neuron is a type of neuron which has one extension from its cell body. This type of neuron contains an axon that has split into two branches. A single process arises from the cell body and then divides into an axon and a dendrite. They develop embryologically as bipolar in shape, and are thus termed pseudounipolar instead of unipolar. A pseudounipolar neuron has one axon that projects from the cell body for relatively a very short distance, before splitting into two branches.
Single-linkage clusteringIn statistics, single-linkage clustering is one of several methods of hierarchical clustering. It is based on grouping clusters in bottom-up fashion (agglomerative clustering), at each step combining two clusters that contain the closest pair of elements not yet belonging to the same cluster as each other. This method tends to produce long thin clusters in which nearby elements of the same cluster have small distances, but elements at opposite ends of a cluster may be much farther from each other than two elements of other clusters.
