Classification

Classification is the action of predicting to which class the new, unlabeled observation actually belongs or, equivalently, what is the value of the (unobserved) modality of the class variable.

classification
decision trees
neural networks
Definition: Classification is a data mining (machine learning) technique used to predict group membership for data instances.

Classification-type problems. Classification-type problems are generally those where we attempt to predict values of a categorical dependent variable (class, group membership, etc.) from one or more continuous and/or categorical predictor ...

Binary classification is the task of classifying the members of a given set of objects into two groups on the basis of whether they have some property or not. Some typical binary classification tasks are ...

Pattern classification
A classic example of pattern classifiction is letter recognition. We are given, for example, a set of pixel values associated with an image of a letter. We want the computer to determine what letter it is.

0 Anaemia Classification
"Anaemia" is a common medical problem. The word anaemia is composed of two Greek roots that together mean "without blood" (Ed-Uthman, 1998).

Although it has mostly used to identify brain regions involved in specific cognitive operations, new pattern classification techniques have been applied to fMRI data in what some have called "mind reading technology.

Classification Assigning a class to a measurement, or equivalently, identifying the probabilistic source of a measurement. The only statistical model that is needed is the conditional model of the class variable given the measurement.

Classification
Now we’ll take a look at the Classify tab. First, take note of the drop down selection that should
currently contain “(Nom) class' for the iris dataset. This allows us to select the attribute to use as ...

Classification
Trees occur in many diverse orders and families of plants, and thus show a wide variety of growth form, leaf types and shapes, bark, reproductive organs, etc.

Classification - Detecting patterns in the input, and determining what class it belongs to.
Sorting chocolates based on visual appearance
Prediction - Given the input, decide what's going to happen next! ...

Classification error
Still another way to measure impurity degree is using index of classification error
Example: Given that Prob (Bus) = 0.4, Prob (Car) = 0.3 and Prob (Train) = 0.3, index of classification error is given as ...

Classification. A key process in the knowledge sharing cycle. Documents are classified and indexed according to their core terms and concepts.

Classification
Automated classification tools such as decision trees have been shown to be very effective for distinguishing and characterizing very large volumes of data.

Classification of life
The classification of living things is called systematics, or taxonomy, and should reflect the evolutionary trees (phylogenetic trees) of the different organisms.

Classification/Pattern recognition:
The task of pattern recognition is to assign an input pattern (like handwritten symbol) to one of many classes. This category includes algorithmic implementations such as associative memory.
...

Pattern Classification A task that neural networks are often trained to do. Given some input pattern, the task is to make an accurate class assignment to the input.

Phoneme Classification
Phoneme classification can be performed with high accuracy by using either static or dynamic approaches. Here we review some typical experiments using each approach.
Static Approaches ...

finite classification a kind of expert system task in which the goal is to classify a given case as one of a prespecified set of possibilities, e.g. diagnosing a disease or identifying an aircraft.

Classification
Clustering algorithms may be classified as listed below:
Exclusive Clustering
Overlapping Clustering ...

classification
The process of dividing a dataset into mutually exclusive groups such that the members of each group are as "close" as possible to one another, and different groups are as "far" as possible from one another, ...

IBM® Classification Module for OmniFind™ Discovery Edition. "The IBM Classification Module is a platform for a wide range of applications that require large amounts of unstructured content to be appropriately categorized and tagged.

Scientific classification: A NASA system learned to classify very faint signals as either stars or galaxies with superhuman accuracy, by studying examples classified by experts; ...

trained on classification data (each output represents one class), and then used directly as classifiers of new data.
trained on (x,f(x)) points of an unknown function f, and then used to interpolate.

The useful classifications are those which match the goals and methods of the machine.

heuristic classification One of the most feasible kinds of expert system given the present knowledge of AI is to put some information in one of a fixed set of categories using several sources of information.

In a pattern classification problem there is no guarantee that your backprop network is going to come up with a sensible way to partition the boundaries between classes. The following is a particular 2D example I cooked up for my book.

classes in classification tasks In a classification task in machine learning, the task is to take each instance and assign it to a particular class.

These include classification algorithms such as decision trees, neural nets, Bayesian classifiers, Support Vector Machines and cased-based (aka non-parametric) learning.

Facial Pattern Classification Techniques.
Face Detection and Eye Localization.
PDBNN Face Recognition System Case Study.

In analytical theory, the best model is one that has the greatest accuracy in predicting all classification states of the target variable and is acceptably robust in its agility to perform well on the validation data set.

[111] The performance of these classifiers have been compared over a wide range of classification tasks[112] in order to find data characteristics that determine classifier performance.

An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurones.

These networks can solve static classification problems such as optical character recognition (OCR). Recurrent Backpropagation is another kind of type used for fixed-point learning. NeuroSolutions, for example, is software that has this ability.

In order that we treat this problem using neural networks, we need to translate it into a pattern classification problem. In other words, we ascribe some unique pattern of activities of the input nodes to a given pair of numbers to be added.

Clustering is a type of multivariate statistical analysis also known as cluster analysis, unsupervised classification analysis, or numerical taxonomy.

(i)The theoretical study of classification
(ii)A hierarchical classification model of a given domain
Term
An agreed name in a medical terminology for a medical condition or treatment ...

The neural network is structured to perform nonlinear Bayesian classification.

Fuzzy logic is able to represent the shades of particular attributes rather than requiring their classification as either one thing or another.

Subject classifications are hyperlinked, so clicking will take you to that subject page. You can access the complete list of subjects by clicking on the medical subjects link on the dictionary homepage.

induction
A logic process that generalizes enumerative concepts, starting from classification and comparison of single examples.
[close the glossary] ...

I have worked on computer vision (my Ph.D. thesis), natural language processing, connectionist/neural net models, and "memory-based reasoning," a method of classification that uses nearest neighbors in a database to aid in making decisions.

See also: Neural network, Data mining, Distribution, Regression, Percept