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59-412: RNN or rnn may refer to: Random neural network , a mathematical representation of an interconnected network of neurons or cells which exchange spiking signals Recurrent neural network , a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence rnn (software) Recursive neural network ,

118-432: A label to instances, and models are trained to correctly predict the preassigned labels of a set of examples). Characterizing the generalization of various learning algorithms is an active topic of current research, especially for deep learning algorithms. Machine learning and statistics are closely related fields in terms of methods, but distinct in their principal goal: statistics draws population inferences from

177-421: A sample , while machine learning finds generalizable predictive patterns. According to Michael I. Jordan , the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics. He also suggested the term data science as a placeholder to call the overall field. Conventional statistical analyses require the a priori selection of a model most suitable for

236-480: A theoretical neural structure formed by certain interactions among nerve cells . Hebb's model of neurons interacting with one another set a groundwork for how AIs and machine learning algorithms work under nodes, or artificial neurons used by computers to communicate data. Other researchers who have studied human cognitive systems contributed to the modern machine learning technologies as well, including logician Walter Pitts and Warren McCulloch , who proposed

295-439: 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 functions can be learned in polynomial time. Negative results show that certain classes cannot be learned in polynomial time. Machine learning approaches are traditionally divided into three broad categories, which correspond to learning paradigms, depending on

354-444: A considerable improvement in learning accuracy. 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. 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,

413-414: A hierarchy of features, with higher-level, more abstract features defined in terms of (or generating) lower-level features. It has been argued that an intelligent machine is one that learns a representation that disentangles the underlying factors of variation that explain the observed data. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that

472-424: A human operator/teacher to recognize patterns and equipped with a " goof " button to cause it to reevaluate incorrect decisions. A representative book on research into machine learning during the 1960s was Nilsson's book on Learning Machines, dealing mostly with machine learning for pattern classification. Interest related to pattern recognition continued into the 1970s, as described by Duda and Hart in 1973. In 1981

531-536: A kind of deep neural network created by applying the same set of weights recursively over a structured input WRNN-TV , branded as Regional News Network, a television station licensed to New Rochelle, New York, United States Rassd News Network , Egypt Reserva Natural Nacional , national park in Colombia IATA code for Bornholm Airport , Denmark rnn , ISO 639-3 code for the Roon language Topics referred to by

590-420: 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 classification algorithm that filters emails, the input would be an incoming email, and the output would be the name of the folder in which to file the email. Examples of regression would be predicting the height of a person, or the future temperature. Similarity learning

649-617: A machine to both learn the features and use them to perform a specific task. 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 data. Examples include dictionary learning, independent component analysis , autoencoders , matrix factorization and various forms of clustering . Manifold learning algorithms attempt to do so under

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708-442: A major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in knowledge discovery and data mining (KDD) the key task is the discovery of previously unknown knowledge. Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in

767-410: A mathematical solution in steady-state which provides the joint probability distribution of the network in terms of the individual probabilities that each cell is excited and able to send out spikes. Computing this solution is based on solving a set of non-linear algebraic equations whose parameters are related to the spiking rates of individual cells and their connectivity to other cells, as well as

826-434: A practical nature. It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics, fuzzy logic , and probability theory . There is a close connection between machine learning and compression. A system that predicts the posterior probabilities of a sequence given its entire history can be used for optimal data compression (by using arithmetic coding on

885-553: A report was given on using teaching strategies so that an artificial neural network learns to recognize 40 characters (26 letters, 10 digits, and 4 special symbols) from a computer terminal. 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 ." This definition of

944-438: A scientific endeavor, machine learning grew out of the quest for artificial intelligence (AI). 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 " neural networks "; these were mostly perceptrons and other models that were later found to be reinventions of

1003-443: A typical KDD task, supervised methods cannot be used due to the unavailability of training data. 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 (for example, in classification, one wants to assign

1062-435: A universal approximator for bounded and continuous functions . Machine learning ML finds application in many fields, including natural language processing , computer vision , speech recognition , email filtering , agriculture , and medicine . The application of ML to business problems is known as predictive analytics . Statistics and mathematical optimization (mathematical programming) methods comprise

1121-772: A zip file's compressed size includes both the zip file and the unzipping software, since you can not unzip it without both, but there may be an even smaller combined form. Examples of AI-powered audio/video compression software include NVIDIA Maxine , AIVC. Examples of software that can perform AI-powered image compression include OpenCV , TensorFlow , MATLAB 's Image Processing Toolbox (IPT) and High-Fidelity Generative Image Compression. 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 . Data compression aims to reduce

1180-509: Is a system with only one input, situation, and only one output, action (or behavior) a. There is neither a separate reinforcement input nor an advice input from the environment. The backpropagated value (secondary reinforcement) is the emotion toward the consequence situation. The CAA exists in two environments, one is the behavioral environment where it behaves, and the other is the genetic environment, wherefrom it initially and only once receives initial emotions about situations to be encountered in

1239-550: 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 verification, and speaker verification. 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

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1298-496: Is different from Wikidata All article disambiguation pages All disambiguation pages Random neural network The random neural network ( RNN ) is a mathematical representation of an interconnected network of neurons or cells which exchange spiking signals . It was invented by Erol Gelenbe and is linked to the G-network model of queueing networks as well as to Gene Regulatory Network models. Each cell state

1357-437: 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 and emotions (feelings) about consequence situations. The system is driven by the interaction between cognition and emotion. The self-learning algorithm updates a memory matrix W =||w(a,s)|| such that in each iteration executes the following machine learning routine: It

1416-444: Is represented by an integer whose value rises when the cell receives an excitatory spike and drops when it receives an inhibitory spike. The spikes can originate outside the network itself, or they can come from other cells in the networks. Cells whose internal excitatory state has a positive value are allowed to send out spikes of either kind to other cells in the network according to specific cell-dependent spiking rates. The model has

1475-432: Is the analysis step of knowledge discovery in databases). Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as " unsupervised learning " or as a preprocessing step to improve learner accuracy. Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being

1534-415: Is thus finding applications in the area of medical diagnostics . 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 (considered representative of

1593-428: 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 in order to train it to classify the cancerous moles. A machine learning algorithm for stock trading may inform the trader of future potential predictions. As

1652-444: The generalized linear models of statistics. Probabilistic reasoning was also employed, especially in automated medical diagnosis . 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. By 1980, expert systems had come to dominate AI, and statistics

1711-506: The "number of features". Most of the dimensionality reduction techniques can be considered as either feature elimination or extraction . One of the popular methods of dimensionality reduction is principal component analysis (PCA). PCA involves changing higher-dimensional data (e.g., 3D) to a smaller space (e.g., 2D). The manifold hypothesis proposes that high-dimensional data sets lie along low-dimensional manifolds , and many dimensionality reduction techniques make this assumption, leading to

1770-482: The AI/CS field, as " connectionism ", by researchers from other disciplines including John Hopfield , David Rumelhart , and Geoffrey Hinton . Their main success came in the mid-1980s with the reinvention of backpropagation . Machine learning (ML), 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

1829-463: The MDP and are used when exact models are infeasible. Reinforcement learning algorithms are used in autonomous vehicles or in learning to play a game against a human opponent. 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

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1888-403: The algorithm to correctly determine the output for inputs that were not a part of the training data. An algorithm that improves the accuracy of its outputs or predictions over time is said to have learned to perform that task. Types of supervised-learning algorithms include active learning , classification and regression . Classification algorithms are used when the outputs are restricted to

1947-452: The area of manifold learning and manifold regularization . 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 . Self-learning, as a machine learning paradigm was introduced in 1982 along with a neural network capable of self-learning, named crossbar adaptive array (CAA). It

2006-540: The arrival rates of spikes from outside the network. The RNN is a recurrent model, i.e. a neural network that is allowed to have complex feedback loops. A highly energy-efficient implementation of random neural networks was demonstrated by Krishna Palem et al. using the Probabilistic CMOS or PCMOS technology and was shown to be c. 226–300 times more efficient in terms of Energy-Performance-Product. RNNs are also related to artificial neural networks , which (like

2065-557: The behavioral environment. After receiving the genome (species) vector from the genetic environment, the CAA learns a goal-seeking behavior, in an environment that contains both desirable and undesirable situations. 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

2124-511: 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 aim to learn low-dimensional representations directly from tensor representations for multidimensional data, without reshaping them into higher-dimensional vectors. Deep learning algorithms discover multiple levels of representation, or

2183-399: The core information of the original data while significantly decreasing the required storage space. 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 (previously) unknown properties in the data (this

2242-558: The data and react based on the presence or absence of such commonalities in each new piece of data. Central applications of unsupervised machine learning include clustering, dimensionality reduction , and density estimation . Cluster analysis is the assignment of a set of observations into subsets (called clusters ) 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

2301-458: 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 training error decreases. But if the hypothesis is too complex, then the model is subject to overfitting and generalization will be poorer. In addition to performance bounds, learning theorists study the time complexity and feasibility of learning. In computational learning theory,

2360-437: The desired output, also known as a supervisory signal. In the mathematical model, each training example is represented by an array or vector, sometimes called a feature vector , and the training data is represented by a matrix . Through iterative optimization of an objective function , supervised learning algorithms learn a function that can be used to predict the output associated with new inputs. An optimal function allows

2419-406: The early mathematical models of neural networks to come up with algorithms that mirror human thought processes. 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

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2478-527: The field is studied in many other disciplines, such as game theory , control theory , operations research , information theory , simulation-based optimization , multi-agent systems , swarm intelligence , statistics and genetic algorithms . In reinforcement learning, the environment is typically represented as a Markov decision process (MDP). Many reinforcements learning algorithms use dynamic programming techniques. Reinforcement learning algorithms do not assume knowledge of an exact mathematical model of

2537-494: The foundations of machine learning. Data mining is a related field of study, focusing on exploratory data analysis (EDA) via unsupervised learning . From a theoretical viewpoint, probably approximately correct (PAC) learning provides a framework for describing machine learning. 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

2596-405: The future is uncertain, learning theory usually does not yield guarantees of the performance of algorithms. Instead, probabilistic bounds on the performance are quite common. The bias–variance decomposition is one way to quantify generalization error . For the best performance in the context of generalization, the complexity of the hypothesis should match the complexity of the function underlying

2655-424: The information in their input but also transform it in a way that makes it useful, often as a pre-processing step before performing classification or predictions. This technique allows reconstruction of the inputs coming from the unknown data-generating distribution, while not being necessarily faithful to configurations that are implausible under that distribution. This replaces manual feature engineering , and allows

2714-434: The machine learning algorithms like Random Forest . Some statisticians have adopted methods from machine learning, leading to a combined field that they call statistical learning . 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

2773-437: The nature of the "signal" or "feedback" available to the learning system: Although each algorithm has advantages and limitations, no single algorithm works for all problems. Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs. The data, known as training data , consists of a set of training examples. Each training example has one or more inputs and

2832-610: The output distribution). Conversely, an optimal compressor can be used for prediction (by finding the symbol that compresses best, given the previous history). This equivalence has been used as a justification for using data compression as a benchmark for "general intelligence". 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

2891-462: The random neural network) have gradient-based learning algorithms . The learning algorithm for an n-node random neural network that includes feedback loops (it is also a recurrent neural network ) is of computational complexity O(n^3) (the number of computations is proportional to the cube of n, the number of neurons). The random neural network can also be used with other learning algorithms such as reinforcement learning . The RNN has been shown to be

2950-403: The same term [REDACTED] This disambiguation page lists articles associated with the title RNN . If an internal link led you here, you may wish to change the link to point directly to the intended article. Retrieved from " https://en.wikipedia.org/w/index.php?title=RNN&oldid=1047223613 " Category : Disambiguation pages Hidden categories: Short description

3009-546: 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 condenses extensive datasets into a more compact set of representative points. Particularly beneficial in image and signal processing , k-means clustering aids in data reduction by replacing groups of data points with their centroids, thereby preserving

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3068-512: The space of occurrences) and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases. 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 (PAC) model. Because training sets are finite and

3127-419: The structure of the data, often defined by some similarity metric and evaluated, for example, by internal compactness , or the similarity between members of the same cluster, and separation , the difference between clusters. Other methods are based on estimated density and graph connectivity . A special type of unsupervised learning called, self-supervised learning involves training a model by generating

3186-525: 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 data shape the model by detecting underlying patterns. The more variables (input) used to train the model, the more accurate the ultimate model will be. Leo Breiman distinguished two statistical modeling paradigms: data model and algorithmic model, wherein "algorithmic model" means more or less

3245-417: The supervisory signal from the data itself. Semi-supervised learning falls between unsupervised learning (without any labeled training data) and supervised learning (with completely labeled training data). 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

3304-428: The tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms. This follows Alan Turing 's proposal in his paper " Computing Machinery and Intelligence ", in which the question "Can machines think?" is replaced with the question "Can machines do what we (as thinking entities) can do?". Modern-day machine learning has two objectives. One

3363-503: The vector norm ||~x||. An exhaustive examination of the feature spaces underlying all compression algorithms is precluded by space; instead, feature vectors chooses to examine three representative lossless compression methods, LZW, LZ77, and PPM. 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,

3422-456: Was also used in this time period. 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 Donald Hebb published the book The Organization of Behavior , in which he introduced

3481-447: Was out of favor. Work on symbolic/knowledge-based learning did continue within AI, leading to inductive logic programming (ILP), but the more statistical line of research was now outside the field of AI proper, in pattern recognition and information retrieval . Neural networks research had been abandoned by AI and computer science around the same time. This line, too, was continued outside

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