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AlaaElhilo/Wikipedia_ComputerScience | Within medical science, pattern recognition is the basis for computer-aided diagnosis systems. CAD describes a procedure that supports the doctor's interpretations and findings. Other typical applications of pattern recognition techniques are automatic speech recognition, speaker identification, classification of text... |
AlaaElhilo/Wikipedia_ComputerScience | Optical character recognition is an example of the application of a pattern classifier. The method of signing one's name was captured with stylus and overlay starting in 1990. The strokes, speed, relative min, relative max, acceleration and pressure is used to uniquely identify and confirm identity. Banks were first of... |
AlaaElhilo/Wikipedia_ComputerScience | Pattern recognition has many real-world applications in image processing. Some examples include: |
AlaaElhilo/Wikipedia_ComputerScience | In psychology, pattern recognition is used to make sense of and identify objects, and is closely related to perception. This explains how the sensory inputs humans receive are made meaningful. Pattern recognition can be thought of in two different ways. The first concerns template matching and the second concerns featu... |
AlaaElhilo/Wikipedia_ComputerScience | Algorithms for pattern recognition depend on the type of label output, on whether learning is supervised or unsupervised, and on whether the algorithm is statistical or non-statistical in nature. Statistical algorithms can further be categorized as generative or discriminative. |
AlaaElhilo/Wikipedia_ComputerScience | Parametric: |
AlaaElhilo/Wikipedia_ComputerScience | Nonparametric: |
AlaaElhilo/Wikipedia_ComputerScience | Unsupervised: |
AlaaElhilo/Wikipedia_ComputerScience | Two different kinds of rule-based systems emerged within the field of artificial intelligence in the 1970s: |
AlaaElhilo/Wikipedia_ComputerScience | The differences and relationships between these two kinds of rule-based system has been a major source of misunderstanding and confusion. |
AlaaElhilo/Wikipedia_ComputerScience | Both kinds of rule-based systems use either forward or backward chaining, in contrast with imperative programs, which execute commands listed sequentially. However, logic programming systems have a logical interpretation, whereas production systems do not. |
AlaaElhilo/Wikipedia_ComputerScience | A classic example of a production rule-based system is the domain-specific expert system that uses rules to make deductions or choices. For example, an expert system might help a doctor choose the correct diagnosis based on a cluster of symptoms, or select tactical moves to play a game. |
AlaaElhilo/Wikipedia_ComputerScience | Rule-based systems can be used to perform lexical analysis to compile or interpret computer programs, or in natural language processing. |
AlaaElhilo/Wikipedia_ComputerScience | Rule-based programming attempts to derive execution instructions from a starting set of data and rules. This is a more indirect method than that employed by an imperative programming language, which lists execution steps sequentially. |
AlaaElhilo/Wikipedia_ComputerScience | A typical rule-based system has four basic components: |
AlaaElhilo/Wikipedia_ComputerScience | Whereas the matching phase of the inference engine has a logical interpretation, the conflict resolution and action phases do not. Instead, "their semantics is usually described as a series of applications of various state-changing operators, which often gets quite involved , and they can hardly be regarded as declarat... |
AlaaElhilo/Wikipedia_ComputerScience | The logic programming family of computer systems includes the programming language Prolog, the database language Datalog and the knowledge representation and problem-solving language Answer Set Programming . In all of these languages, rules are written in the form of clauses: |
AlaaElhilo/Wikipedia_ComputerScience | and are read as declarative sentences in logical form: |
AlaaElhilo/Wikipedia_ComputerScience | In the simplest case of Horn clauses , which are a subset of first-order logic, all of the A, B1, ..., Bn are atomic formulae. |
AlaaElhilo/Wikipedia_ComputerScience | Although Horn clause logic programs are Turing complete, for many practical applications, it is useful to extend Horn clause programs by allowing negative conditions, implemented by negation as failure. Such extended logic programs have the knowledge representation capabilities of a non-monotonic logic. |
AlaaElhilo/Wikipedia_ComputerScience | The most obvious difference between the two kinds of systems is that production rules are typically written in the forward direction, if A then B, and logic programming rules are typically written in the backward direction, B if A. In the case of logic programming rules, this difference is superficial and purely synta... |
AlaaElhilo/Wikipedia_ComputerScience | In the case of production rules, the forward direction of the syntax reflects the stimulus-response character of most production rules, with the stimulus A coming before the response B. Moreover, even in cases when the response is simply to draw a conclusion B from an assumption A, as in modus ponens, the match-resolve... |
AlaaElhilo/Wikipedia_ComputerScience | In his Introduction to Cognitive Science, Paul Thagard includes logic and rules as alternative approaches to modelling human thinking. He does not consider logic programs in general, but he considers Prolog to be, not a rule-based system, but "a programming language that uses logic representations and deductive techniq... |
AlaaElhilo/Wikipedia_ComputerScience | He argues that rules, which have the form IF condition THEN action, are "very similar" to logical conditionals, but they are simpler and have greater psychological plausability . Among other differences between logic and rules, he argues that logic uses deduction, but rules use search and can be used to reason either ... |
AlaaElhilo/Wikipedia_ComputerScience | Machine learning is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. Recently, artificial neural networks have been able to surpass many previous a... |
AlaaElhilo/Wikipedia_ComputerScience | Machine learning approaches have been applied to many fields including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine. ML is known in its application across business problems under the name predictive analytics. Although not all machine learning is statistic... |
AlaaElhilo/Wikipedia_ComputerScience | The mathematical foundations of ML are provided by mathematical optimization methods. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning. |
AlaaElhilo/Wikipedia_ComputerScience | From a theoretical viewpoint, probably approximately correct learning provides a framework for describing machine learning. |
AlaaElhilo/Wikipedia_ComputerScience | The term machine learning was coined in 1959 by Arthur Samuel, an IBM employee and pioneer in the field of computer gaming and artificial intelligence. The synonym self-teaching computers was also used in this time period. |
AlaaElhilo/Wikipedia_ComputerScience | Although the earliest machine learning model was introduced in the 1950s when Arthur Samuel invented a program that calculated the winning chance in checkers for each side, the history of machine learning roots back to decades of human desire and effort to study human cognitive processes. In 1949, Canadian psychologist... |
AlaaElhilo/Wikipedia_ComputerScience | By the early 1960s an experimental "learning machine" with punched tape memory, called Cybertron, had been developed by Raytheon Company to analyze sonar signals, electrocardiograms, and speech patterns using rudimentary reinforcement learning. It was repetitively "trained" by a human operator/teacher to recognize patt... |
AlaaElhilo/Wikipedia_ComputerScience | Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E... |
AlaaElhilo/Wikipedia_ComputerScience | Modern-day machine learning has two objectives. One is to classify data based on models which have been developed; the other purpose is to make predictions for future outcomes based on these models. A hypothetical algorithm specific to classifying data may use computer vision of moles coupled with supervised learning ... |
AlaaElhilo/Wikipedia_ComputerScience | As a scientific endeavor, machine learning grew out of the quest for artificial intelligence . In the early days of AI as an academic discipline, some researchers were interested in having machines learn from data. They attempted to approach the problem with various symbolic methods, as well as what were then termed "n... |
AlaaElhilo/Wikipedia_ComputerScience | However, an increasing emphasis on the logical, knowledge-based approach caused a rift between AI and machine learning. Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation.: 488 By 1980, expert systems had come to dominate AI, and statistics was out of favor.... |
AlaaElhilo/Wikipedia_ComputerScience | Machine learning , reorganized and recognized as its own field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and mod... |
AlaaElhilo/Wikipedia_ComputerScience | An alternative view can show compression algorithms implicitly map strings into implicit feature space vectors, and compression-based similarity measures compute similarity within these feature spaces. For each compressor C we define an associated vector space ℵ, such that C maps an input string x, corresponding to the... |
AlaaElhilo/Wikipedia_ComputerScience | According to AIXI theory, a connection more directly explained in Hutter Prize, the best possible compression of x is the smallest possible software that generates x. For example, in that model, a zip file's compressed size includes both the zip file and the unzipping software, since you can not unzip it without both, ... |
AlaaElhilo/Wikipedia_ComputerScience | Examples of AI-powered audio/video compression software include VP9, NVIDIA Maxine, AIVC, AccMPEG. Examples of software that can perform AI-powered image compression include OpenCV, TensorFlow, MATLAB's Image Processing Toolbox and High-Fidelity Generative Image Compression. |
AlaaElhilo/Wikipedia_ComputerScience | In unsupervised machine learning, k-means clustering can be utilized to compress data by grouping similar data points into clusters. This technique simplifies handling extensive datasets that lack predefined labels and finds widespread use in fields such as image compression. |
AlaaElhilo/Wikipedia_ComputerScience | Data compression aims to reduce the size of data files, enhancing storage efficiency and speeding up data transmission. K-means clustering, an unsupervised machine learning algorithm, is employed to partition a dataset into a specified number of clusters, k, each represented by the centroid of its points. This process ... |
AlaaElhilo/Wikipedia_ComputerScience | Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of unknown properties in the data . Data mining uses many machine learning methods, ... |
AlaaElhilo/Wikipedia_ComputerScience | Machine learning also has intimate ties to optimization: many learning problems are formulated as minimization of some loss function on a training set of examples. Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances . |
AlaaElhilo/Wikipedia_ComputerScience | The difference between optimization and machine learning arises from the goal of generalization: while optimization algorithms can minimize the loss on a training set, machine learning is concerned with minimizing the loss on unseen samples. Characterizing the generalization of various learning algorithms is an active ... |
AlaaElhilo/Wikipedia_ComputerScience | Machine learning and statistics are closely related fields in terms of methods, but distinct in their principal goal: statistics draws population inferences from a sample, while machine learning finds generalizable predictive patterns. According to Michael I. Jordan, the ideas of machine learning, from methodological p... |
AlaaElhilo/Wikipedia_ComputerScience | Conventional statistical analyses require the a priori selection of a model most suitable for the study data set. In addition, only significant or theoretically relevant variables based on previous experience are included for analysis. In contrast, machine learning is not built on a pre-structured model; rather, the da... |
AlaaElhilo/Wikipedia_ComputerScience | Leo Breiman distinguished two statistical modeling paradigms: data model and algorithmic model, wherein "algorithmic model" means more or less the machine learning algorithms like Random Forest. |
AlaaElhilo/Wikipedia_ComputerScience | Some statisticians have adopted methods from machine learning, leading to a combined field that they call statistical learning. |
AlaaElhilo/Wikipedia_ComputerScience | Analytical and computational techniques derived from deep-rooted physics of disordered systems can be extended to large-scale problems, including machine learning, e.g., to analyze the weight space of deep neural networks. Statistical physics is thus finding applications in the area of medical diagnostics. |
AlaaElhilo/Wikipedia_ComputerScience | A core objective of a learner is to generalize from its experience. Generalization in this context is the ability of a learning machine to perform accurately on new, unseen examples/tasks after having experienced a learning data set. The training examples come from some generally unknown probability distribution and t... |
AlaaElhilo/Wikipedia_ComputerScience | The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory via the Probably Approximately Correct Learning model. Because training sets are finite and the future is uncertain, learning theory usually does not yield ... |
AlaaElhilo/Wikipedia_ComputerScience | For the best performance in the context of generalization, the complexity of the hypothesis should match the complexity of the function underlying the data. If the hypothesis is less complex than the function, then the model has under fitted the data. If the complexity of the model is increased in response, then the tr... |
AlaaElhilo/Wikipedia_ComputerScience | In addition to performance bounds, learning theorists study the time complexity and feasibility of learning. In computational learning theory, a computation is considered feasible if it can be done in polynomial time. There are two kinds of time complexity results: Positive results show that a certain class of function... |
AlaaElhilo/Wikipedia_ComputerScience | Machine learning approaches are traditionally divided into three broad categories, which correspond to learning paradigms, depending on the nature of the "signal" or "feedback" available to the learning system: |
AlaaElhilo/Wikipedia_ComputerScience | Although each algorithm has advantages and limitations, no single algorithm works for all problems. |
AlaaElhilo/Wikipedia_ComputerScience | Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs. The data is known as training data, and consists of a set of training examples. Each training example has one or more inputs and the desired output, also known as a supervisory signal. In t... |
AlaaElhilo/Wikipedia_ComputerScience | Types of supervised-learning algorithms include active learning, classification and regression. Classification algorithms are used when the outputs are restricted to a limited set of values, and regression algorithms are used when the outputs may have any numerical value within a range. As an example, for a classificat... |
AlaaElhilo/Wikipedia_ComputerScience | Similarity learning is an area of supervised machine learning closely related to regression and classification, but the goal is to learn from examples using a similarity function that measures how similar or related two objects are. It has applications in ranking, recommendation systems, visual identity tracking, face ... |
AlaaElhilo/Wikipedia_ComputerScience | Unsupervised learning algorithms find structures in data that has not been labeled, classified or categorized. Instead of responding to feedback, unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. Central applic... |
AlaaElhilo/Wikipedia_ComputerScience | Cluster analysis is the assignment of a set of observations into subsets so that observations within the same cluster are similar according to one or more predesignated criteria, while observations drawn from different clusters are dissimilar. Different clustering techniques make different assumptions on the structure... |
AlaaElhilo/Wikipedia_ComputerScience | Semi-supervised learning falls between unsupervised learning and supervised learning . Some of the training examples are missing training labels, yet many machine-learning researchers have found that unlabeled data, when used in conjunction with a small amount of labeled data, can produce a considerable improvement in... |
AlaaElhilo/Wikipedia_ComputerScience | In weakly supervised learning, the training labels are noisy, limited, or imprecise; however, these labels are often cheaper to obtain, resulting in larger effective training sets. |
AlaaElhilo/Wikipedia_ComputerScience | Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. Due to its generality, the field is studied in many other disciplines, such as game theory, control theory, operations research, informatio... |
AlaaElhilo/Wikipedia_ComputerScience | Dimensionality reduction is a process of reducing the number of random variables under consideration by obtaining a set of principal variables. In other words, it is a process of reducing the dimension of the feature set, also called the "number of features". Most of the dimensionality reduction techniques can be consi... |
AlaaElhilo/Wikipedia_ComputerScience | Other approaches have been developed which do not fit neatly into this three-fold categorization, and sometimes more than one is used by the same machine learning system. For example, topic modeling, meta-learning. |
AlaaElhilo/Wikipedia_ComputerScience | Self-learning, as a machine learning paradigm was introduced in 1982 along with a neural network capable of self-learning, named crossbar adaptive array . It is learning with no external rewards and no external teacher advice. The CAA self-learning algorithm computes, in a crossbar fashion, both decisions about actions... |
AlaaElhilo/Wikipedia_ComputerScience | in situation s perform action a |
AlaaElhilo/Wikipedia_ComputerScience | receive a consequence situation s' |
AlaaElhilo/Wikipedia_ComputerScience | compute emotion of being in the consequence situation v |
AlaaElhilo/Wikipedia_ComputerScience | update crossbar memory w' = w + v |
AlaaElhilo/Wikipedia_ComputerScience | It is a system with only one input, situation, and only one output, action a. There is neither a separate reinforcement input nor an advice input from the environment. The backpropagated value is the emotion toward the consequence situation. The CAA exists in two environments, one is the behavioral environment where ... |
AlaaElhilo/Wikipedia_ComputerScience | Several learning algorithms aim at discovering better representations of the inputs provided during training. Classic examples include principal component analysis and cluster analysis. Feature learning algorithms, also called representation learning algorithms, often attempt to preserve the information in their input ... |
AlaaElhilo/Wikipedia_ComputerScience | Feature learning can be either supervised or unsupervised. In supervised feature learning, features are learned using labeled input data. Examples include artificial neural networks, multilayer perceptrons, and supervised dictionary learning. In unsupervised feature learning, features are learned with unlabeled input d... |
AlaaElhilo/Wikipedia_ComputerScience | Manifold learning algorithms attempt to do so under the constraint that the learned representation is low-dimensional. Sparse coding algorithms attempt to do so under the constraint that the learned representation is sparse, meaning that the mathematical model has many zeros. Multilinear subspace learning algorithms ai... |
AlaaElhilo/Wikipedia_ComputerScience | Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process. However, real-world data such as images, video, and sensory data has not yielded attempts to algorithmically define specific features. An alt... |
AlaaElhilo/Wikipedia_ComputerScience | Sparse dictionary learning is a feature learning method where a training example is represented as a linear combination of basis functions, and is assumed to be a sparse matrix. The method is strongly NP-hard and difficult to solve approximately. A popular heuristic method for sparse dictionary learning is the K-SVD al... |
AlaaElhilo/Wikipedia_ComputerScience | In data mining, anomaly detection, also known as outlier detection, is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically, the anomalous items represent an issue such as bank fraud, a structural defect, medical problems or ... |
AlaaElhilo/Wikipedia_ComputerScience | In particular, in the context of abuse and network intrusion detection, the interesting objects are often not rare objects, but unexpected bursts of inactivity. This pattern does not adhere to the common statistical definition of an outlier as a rare object. Many outlier detection methods will fail on such data unless... |
AlaaElhilo/Wikipedia_ComputerScience | Three broad categories of anomaly detection techniques exist. Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal, by looking for instances that seem to fit the least to the remainder of the data set.... |
AlaaElhilo/Wikipedia_ComputerScience | Robot learning is inspired by a multitude of machine learning methods, starting from supervised learning, reinforcement learning, and finally meta-learning . |
AlaaElhilo/Wikipedia_ComputerScience | Association rule learning is a rule-based machine learning method for discovering relationships between variables in large databases. It is intended to identify strong rules discovered in databases using some measure of "interestingness". |
AlaaElhilo/Wikipedia_ComputerScience | Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves "rules" to store, manipulate or apply knowledge. The defining characteristic of a rule-based machine learning algorithm is the identification and utilization of a set of relational rules that collectively r... |
AlaaElhilo/Wikipedia_ComputerScience | Based on the concept of strong rules, Rakesh Agrawal, Tomasz Imieliński and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale systems in supermarkets. For example, the rule
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AlaaElhilo/Wikipedia_ComputerScience | Learning classifier systems are a family of rule-based machine learning algorithms that combine a discovery component, typically a genetic algorithm, with a learning component, performing either supervised learning, reinforcement learning, or unsupervised learning. They seek to identify a set of context-dependent rule... |
AlaaElhilo/Wikipedia_ComputerScience | Inductive logic programming is an approach to rule learning using logic programming as a uniform representation for input examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hyp... |
AlaaElhilo/Wikipedia_ComputerScience | Inductive logic programming is particularly useful in bioinformatics and natural language processing. Gordon Plotkin and Ehud Shapiro laid the initial theoretical foundation for inductive machine learning in a logical setting. Shapiro built their first implementation in 1981: a Prolog program that inductively inferred... |
AlaaElhilo/Wikipedia_ComputerScience | A machine learning model is a type of mathematical model which, after being "trained" on a given dataset, can be used to make predictions or classifications on new data. During training, a learning algorithm iteratively adjusts the model's internal parameters to minimize errors in its predictions. By extension the term... |
AlaaElhilo/Wikipedia_ComputerScience | Various types of models have been used and researched for machine learning systems, picking the best model for a task is called model selection. |
AlaaElhilo/Wikipedia_ComputerScience | Artificial neural networks , or connectionist systems, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules. |
AlaaElhilo/Wikipedia_ComputerScience | An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receive... |
AlaaElhilo/Wikipedia_ComputerScience | The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, mach... |
AlaaElhilo/Wikipedia_ComputerScience | Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition. |
AlaaElhilo/Wikipedia_ComputerScience | Decision tree learning uses a decision tree as a predictive model to go from observations about an item to conclusions about the item's target value . It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of ... |
AlaaElhilo/Wikipedia_ComputerScience | Support-vector machines , also known as support-vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example fall... |
AlaaElhilo/Wikipedia_ComputerScience | Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squar... |
AlaaElhilo/Wikipedia_ComputerScience | A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph . For example, a Bayesian network could represent the probabilistic relationships between diseases and sym... |
AlaaElhilo/Wikipedia_ComputerScience | A Gaussian process is a stochastic process in which every finite collection of the random variables in the process has a multivariate normal distribution, and it relies on a pre-defined covariance function, or kernel, that models how pairs of points relate to each other depending on their locations. |
AlaaElhilo/Wikipedia_ComputerScience | Given a set of observed points, or input–output examples, the distribution of the output of a new point as function of its input data can be directly computed by looking like the observed points and the covariances between those points and the new, unobserved point. |
AlaaElhilo/Wikipedia_ComputerScience | Gaussian processes are popular surrogate models in Bayesian optimization used to do hyperparameter optimization. |
AlaaElhilo/Wikipedia_ComputerScience | A genetic algorithm is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s. Conv... |
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