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Cognitive computer : A cognitive computer is a computer that hardwires artificial intelligence and machine learning algorithms into an integrated circuit that closely reproduces the behavior of the human brain. It generally adopts a neuromorphic engineering approach. Synonyms include neuromorphic chip and cognitive chi... |
Cognitive computer : TrueNorth was a neuromorphic CMOS integrated circuit produced by IBM in 2014. It is a manycore processor network on a chip design, with 4096 cores, each one having 256 programmable simulated neurons for a total of just over a million neurons. In turn, each neuron has 256 programmable "synapses" tha... |
Cognitive computer : In 2023, IBM released its NorthPole chip, which is a proof-of-concept for dramatically improving performance by intertwining compute with memory on-chip, thus eliminating the Von Neumann bottleneck. It blends approaches from IBM's 2014 TrueNorth system with modern hardware designs to achieve speeds... |
Cognitive computer : SpiNNaker (Spiking Neural Network Architecture) is a massively parallel, manycore supercomputer architecture designed by the Advanced Processor Technologies Research Group at the Department of Computer Science, University of Manchester. |
Cognitive computer : Critics argue that a room-sized computer – as in the case of IBM's Watson – is not a viable alternative to a three-pound human brain. Some also cite the difficulty for a single system to bring so many elements together, such as the disparate sources of information as well as computing resources. In... |
Cognitive computer : AI accelerator Cognitive computing Computational cognition Neuromorphic engineering Tensor Processing Unit Turing test Spiking neural network |
Cognitive computer : CES 2018: Intel gives glimpse into mind-blowing future of computing Schank, Roger C.; Childers, Peter G. (1984). The cognitive computer: on language, learning, and artificial intelligence. Addison-Wesley Pub. Co. ISBN 9780201064438. Wilson, Stephen (1988). "The Cognitive Computer: On Language, Lear... |
Conceptual clustering : Conceptual clustering is a machine learning paradigm for unsupervised classification that has been defined by Ryszard S. Michalski in 1980 (Fisher 1987, Michalski 1980) and developed mainly during the 1980s. It is distinguished from ordinary data clustering by generating a concept description fo... |
Conceptual clustering : Conceptual clustering is obviously closely related to data clustering; however, in conceptual clustering it is not only the inherent structure of the data that drives cluster formation, but also the Description language which is available to the learner. Thus, a statistically strong grouping in ... |
Conceptual clustering : A fair number of algorithms have been proposed for conceptual clustering. Some examples are given below: CLUSTER/2 (Michalski & Stepp 1983) COBWEB (Fisher 1987) CYRUS (Kolodner 1983) GALOIS (Carpineto & Romano 1993), GCF (Talavera & Béjar 2001) INC (Hadzikadic & Yun 1989) ITERATE (Biswas, Weinbe... |
Conceptual clustering : This section discusses the rudiments of the conceptual clustering algorithm COBWEB. There are many other algorithms using different heuristics and "category goodness" or category evaluation criteria, but COBWEB is one of the best known. The reader is referred to the bibliography for other method... |
Conceptual clustering : Bibliography of conceptual clustering Working python implementation of COBWEB |
Hebbian theory : Hebbian theory is a neuropsychological theory claiming that an increase in synaptic efficacy arises from a presynaptic cell's repeated and persistent stimulation of a postsynaptic cell. It is an attempt to explain synaptic plasticity, the adaptation of neurons during the learning process. Hebbian theor... |
Hebbian theory : Hebbian theory provides an explanation for how neurons might connect to become engrams, which may be stored in overlapping cell assemblies, or groups of neurons that encode specific information. Initially created as a way to explain recurrent activity in specific groups of cortical neurons, Hebb's theo... |
Hebbian theory : From the point of view of artificial neurons and artificial neural networks, Hebb's principle can be described as a method of determining how to alter the weights between model neurons. The weight between two neurons increases if the two neurons activate simultaneously, and reduces if they activate sep... |
Hebbian theory : Because of the simple nature of Hebbian learning, based only on the coincidence of pre- and post-synaptic activity, it may not be intuitively clear why this form of plasticity leads to meaningful learning. However, it can be shown that Hebbian plasticity does pick up the statistical properties of the i... |
Hebbian theory : Hebbian learning and spike-timing-dependent plasticity have been used in an influential theory of how mirror neurons emerge. Mirror neurons are neurons that fire both when an individual performs an action and when the individual sees or hears another perform a similar action. The discovery of these neu... |
Hebbian theory : Hebbian learning is linked to cognitive processes like decision-making and social learning. Cognitive neuroscience has started to explore the intersection of Hebbian theory with brain regions responsible for reward processing and social cognition, such as the striatum and prefrontal cortex. It is hypot... |
Hebbian theory : Despite the common use of Hebbian models for long-term potentiation, Hebbian theory does not cover all forms of long-term synaptic plasticity. Hebb did not propose any rules for inhibitory synapses or predictions for anti-causal spike sequences (where the presynaptic neuron fires after the postsynaptic... |
Hebbian theory : Modern research has expanded upon Hebb's original ideas. Spike-timing-dependent plasticity (STDP), for example, refines Hebbian principles by incorporating the precise timing of neuronal spikes to Hebbian theory. Experimental advancements have also linked Hebbian learning to complex behaviors, such as ... |
Hebbian theory : Hebb, D.O. (1961). "Distinctive features of learning in the higher animal". In J. F. Delafresnaye (ed.). Brain Mechanisms and Learning. London: Oxford University Press. Hebb, D. O. (1940). "Human Behavior After Extensive Bilateral Removal from the Frontal Lobes". Archives of Neurology and Psychiatry. 4... |
Hebbian theory : Overview Archived 2017-05-02 at the Wayback Machine Hebbian Learning tutorial (Part 1: Novelty Filtering, Part 2: PCA) |
Isolation forest : Isolation Forest is an algorithm for data anomaly detection using binary trees. It was developed by Fei Tony Liu in 2008. It has a linear time complexity and a low memory use, which works well for high-volume data. It is based on the assumption that because anomalies are few and different from other ... |
Isolation forest : The Isolation Forest (iForest) algorithm was initially proposed by Fei Tony Liu, Kai Ming Ting and Zhi-Hua Zhou in 2008. In 2012 the same authors showed that iForest has linear time complexity, a small memory requirement, and is applicable to high-dimensional data. In 2010, an extension of the algori... |
Isolation forest : The premise of the Isolation Forest algorithm is that anomalous data points are easier to separate from the rest of the sample. In order to isolate a data point, the algorithm recursively generates partitions on the sample by randomly selecting an attribute and then randomly selecting a split value b... |
Isolation forest : Anomaly detection with Isolation Forest is done as follows: Use the training dataset to build some number of iTrees For each data point in the test set: Pass it through all the iTrees, counting the path length for each tree Assign an “anomaly score” to the instance Label the point as “anomaly” if its... |
Isolation forest : The Isolation Forest algorithm has shown its effectiveness in spotting anomalies in data sets like uncovering credit card fraud instances among transactions, by European cardholders with an unbalanced dataset where it can distinguish fraudulent activities from legitimate ones by identifying rare patt... |
Isolation forest : Sub-sampling: Because iForest does not need to isolate normal instances, it can often ignore most of the training set. Thus, it works very well when the sampling size is kept small, unlike most other methods, which benefit from a large sample size. Swamping: When normal instances are too close to ano... |
Isolation forest : The performance of the Isolation Forest algorithm is highly dependent on the selection of its parameters. Properly tuning these parameters can significantly enhance the algorithm's ability to accurately identify anomalies. Understanding the role and impact of each parameter is crucial for optimizing ... |
Isolation forest : SCiForest (Isolation Forest with Split-selection Criterion) is an extension of the original Isolation Forest algorithm, specifically designed to target clustered anomalies. It introduces a split-selection criterion and uses random hyper-planes that are non-axis-parallel to the original attributes. SC... |
Isolation forest : Extended Isolation Forest (Extended IF or EIF) is another extension of the original Isolation Forest algorithm. Extended IF uses rotated trees in different planes, similarly to SCiForest and random values are selected to split the data, such as a random slope or intercept. The standard Isolation Fore... |
Isolation forest : Original implementation by Fei Tony Liu is Isolation Forest in R. Other implementations (in alphabetical order): ELKI contains a Java implementation. Isolation Forest - A distributed Spark/Scala implementation with Open Neural Network Exchange (ONNX) export for easy cross-platform inference. Isolatio... |
Isolation forest : The isolation forest algorithm is commonly used by data scientists through the version made available in the scikit-learn library. The snippet below depicts a brief implementation of an isolation forest, with direct explanations with comments. In this snippet we can observe the simplicity of a standa... |
Isolation forest : Anomaly detection == References == |
Node2vec : node2vec is an algorithm to generate vector representations of nodes on a graph. The node2vec framework learns low-dimensional representations for nodes in a graph through the use of random walks through a graph starting at a target node. It is useful for a variety of machine learning applications. node2vec ... |
Node2vec : Struc2vec Graph Neural Network == References == |
RAMnets : RAMnets is one of the oldest practical neurally inspired classification algorithms. The RAMnets is also known as a type of "n-tuple recognition method" or "weightless neural network". |
RAMnets : Consider (let us say N) sets of n distinct bit locations are selected randomly. These are the n-tuples. The restriction of a pattern to an n-tuple can be regarded as an n-bit number which, together with the identity of the n-tuple, constitutes a `feature' of the pattern. The standard n-tuple recognizer operat... |
RAMnets : The RAMnets formed the basis of a commercial product known as WiSARD (Wilkie, Stonham and Aleksander Recognition Device) was the first artificial neural network machine to be patented. A RAM-discriminator consists of a set of X one-bit word RAMs with n inputs and a summing device (Σ). Any such RAM-discriminat... |
RAMnets : Artificial Neural Network Kronecker delta Pattern Recognition Unsupervised learning Erlang distribution Machine learning Erlang (unit) |
RAMnets : Michal Morciniec and Richard Rohwer(1995) "The n-tuple Classifier: Too Good to Ignore" Hastie, Trevor; Tibshirani, Robert (2009). The Elements of Statistical Learning: Data mining, Inference, and Prediction. New York: Springer. pp. 485–586. doi:10.1007/978-0-387-84858-7_14. ISBN 978-0-387-84857-0. Hinton, Geo... |
RAMnets : N. M. Allinson, A. R. Kolcz (1997). N-Tuple Neural Networks. Springer Science+Business Media New York: Springer, Boston, MA. ISBN 978-1-4615-6099-9. Fukunaga, Keinosuke (1990). Introduction to Statistical Pattern Recognition (2nd ed.). Boston: Academic Press. ISBN 0-12-269851-7. Hornegger, Joachim; Paulus, Di... |
Sparse dictionary learning : Sparse dictionary learning (also known as sparse coding or SDL) is a representation learning method which aims to find a sparse representation of the input data in the form of a linear combination of basic elements as well as those basic elements themselves. These elements are called atoms,... |
Sparse dictionary learning : Given the input dataset X = [ x 1 , . . . , x K ] , x i ∈ R d ,...,x_],x_\in \mathbb ^ we wish to find a dictionary D ∈ R d × n : D = [ d 1 , . . . , d n ] \in \mathbb ^:D=[d_,...,d_] and a representation R = [ r 1 , . . . , r K ] , r i ∈ R n ,...,r_],r_\in \mathbb ^ such that both ‖ X ... |
Sparse dictionary learning : As the optimization problem described above can be solved as a convex problem with respect to either dictionary or sparse coding while the other one of the two is fixed, most of the algorithms are based on the idea of iteratively updating one and then the other. The problem of finding an op... |
Sparse dictionary learning : The dictionary learning framework, namely the linear decomposition of an input signal using a few basis elements learned from data itself, has led to state-of-art results in various image and video processing tasks. This technique can be applied to classification problems in a way that if w... |
Sparse dictionary learning : Sparse approximation Sparse PCA K-SVD Matrix factorization Neural sparse coding == References == |
Struc2vec : struc2vec is a framework to generate node vector representations on a graph that preserve the structural identity. In contrast to node2vec, that optimizes node embeddings so that nearby nodes in the graph have similar embedding, struc2vec captures the roles of nodes in a graph, even if structurally similar ... |
Vector quantization : Vector quantization (VQ) is a classical quantization technique from signal processing that allows the modeling of probability density functions by the distribution of prototype vectors. Developed in the early 1980s by Robert M. Gray, it was originally used for data compression. It works by dividin... |
Vector quantization : The simplest training algorithm for vector quantization is: Pick a sample point at random Move the nearest quantization vector centroid towards this sample point, by a small fraction of the distance Repeat A more sophisticated algorithm reduces the bias in the density matching estimation, and ensu... |
Vector quantization : Vector quantization is used for lossy data compression, lossy data correction, pattern recognition, density estimation and clustering. Lossy data correction, or prediction, is used to recover data missing from some dimensions. It is done by finding the nearest group with the data dimensions availa... |
Vector quantization : Subtopics Related topics Part of this article was originally based on material from the Free On-line Dictionary of Computing and is used with permission under the GFDL. |
Vector quantization : http://www.data-compression.com/vq.html Archived 2017-12-10 at the Wayback Machine QccPack — Quantization, Compression, and Coding Library (open source) VQ Indexes Compression and Information Hiding Using Hybrid Lossless Index Coding, Wen-Jan Chen and Wen-Tsung Huang |
Support vector machine : In machine learning, support vector machines (SVMs, also support vector networks) are supervised max-margin models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratories, SVMs are one of the most studied models, bein... |
Support vector machine : Classifying data is a common task in machine learning. Suppose some given data points each belong to one of two classes, and the goal is to decide which class a new data point will be in. In the case of support vector machines, a data point is viewed as a p -dimensional vector (a list of p nu... |
Support vector machine : SVMs can be used to solve various real-world problems: SVMs are helpful in text and hypertext categorization, as their application can significantly reduce the need for labeled training instances in both the standard inductive and transductive settings. Some methods for shallow semantic parsing... |
Support vector machine : The original SVM algorithm was invented by Vladimir N. Vapnik and Alexey Ya. Chervonenkis in 1964. In 1992, Bernhard Boser, Isabelle Guyon and Vladimir Vapnik suggested a way to create nonlinear classifiers by applying the kernel trick to maximum-margin hyperplanes. The "soft margin" incarnatio... |
Support vector machine : We are given a training dataset of n points of the form ( x 1 , y 1 ) , … , ( x n , y n ) , _,y_),\ldots ,(\mathbf _,y_), where the y i are either 1 or −1, each indicating the class to which the point x i _ belongs. Each x i _ is a p -dimensional real vector. We want to find the "maximum... |
Support vector machine : The original maximum-margin hyperplane algorithm proposed by Vapnik in 1963 constructed a linear classifier. However, in 1992, Bernhard Boser, Isabelle Guyon and Vladimir Vapnik suggested a way to create nonlinear classifiers by applying the kernel trick (originally proposed by Aizerman et al.)... |
Support vector machine : Computing the (soft-margin) SVM classifier amounts to minimizing an expression of the form We focus on the soft-margin classifier since, as noted above, choosing a sufficiently small value for λ yields the hard-margin classifier for linearly classifiable input data. The classical approach, whi... |
Support vector machine : The soft-margin support vector machine described above is an example of an empirical risk minimization (ERM) algorithm for the hinge loss. Seen this way, support vector machines belong to a natural class of algorithms for statistical inference, and many of its unique features are due to the beh... |
Support vector machine : SVMs belong to a family of generalized linear classifiers and can be interpreted as an extension of the perceptron. They can also be considered a special case of Tikhonov regularization. A special property is that they simultaneously minimize the empirical classification error and maximize the ... |
Support vector machine : The parameters of the maximum-margin hyperplane are derived by solving the optimization. There exist several specialized algorithms for quickly solving the quadratic programming (QP) problem that arises from SVMs, mostly relying on heuristics for breaking the problem down into smaller, more man... |
Support vector machine : In situ adaptive tabulation Kernel machines Fisher kernel Platt scaling Polynomial kernel Predictive analytics Regularization perspectives on support vector machines Relevance vector machine, a probabilistic sparse-kernel model identical in functional form to SVM Sequential minimal optimization... |
Support vector machine : Bennett, Kristin P.; Campbell, Colin (2000). "Support Vector Machines: Hype or Hallelujah?" (PDF). SIGKDD Explorations. 2 (2): 1–13. doi:10.1145/380995.380999. S2CID 207753020. Cristianini, Nello; Shawe-Taylor, John (2000). An Introduction to Support Vector Machines and other kernel-based learn... |
Support vector machine : libsvm, LIBSVM is a popular library of SVM learners liblinear is a library for large linear classification including some SVMs SVM light is a collection of software tools for learning and classification using SVM SVMJS live demo Archived 2013-05-05 at the Wayback Machine is a GUI demo for JavaS... |
Hinge loss : In machine learning, the hinge loss is a loss function used for training classifiers. The hinge loss is used for "maximum-margin" classification, most notably for support vector machines (SVMs). For an intended output t = ±1 and a classifier score y, the hinge loss of the prediction y is defined as ℓ ( y )... |
Hinge loss : While binary SVMs are commonly extended to multiclass classification in a one-vs.-all or one-vs.-one fashion, it is also possible to extend the hinge loss itself for such an end. Several different variations of multiclass hinge loss have been proposed. For example, Crammer and Singer defined it for a linea... |
Hinge loss : The hinge loss is a convex function, so many of the usual convex optimizers used in machine learning can work with it. It is not differentiable, but has a subgradient with respect to model parameters w of a linear SVM with score function y = w ⋅ x \cdot \mathbf that is given by ∂ ℓ ∂ w i = =-t\cdot x_&t... |
Hinge loss : Multivariate adaptive regression spline § Hinge functions == References == |
Least-squares support vector machine : Least-squares support-vector machines (LS-SVM) for statistics and in statistical modeling, are least-squares versions of support-vector machines (SVM), which are a set of related supervised learning methods that analyze data and recognize patterns, and which are used for classific... |
Least-squares support vector machine : Given a training set i = 1 N ,y_\_^ with input data x i ∈ R n \in \mathbb ^ and corresponding binary class labels y i ∈ \in \ , the SVM classifier, according to Vapnik's original formulation, satisfies the following conditions: w^\phi (x_)+b\geq 1,&\quad y_=+1,\\w^\phi (x_)+b\l... |
Least-squares support vector machine : A Bayesian interpretation of the SVM has been proposed by Smola et al. They showed that the use of different kernels in SVM can be regarded as defining different prior probability distributions on the functional space, as P [ f ] ∝ exp ( − β ‖ P ^ f ‖ 2 ) f\right\|^\right) . Her... |
Least-squares support vector machine : J. A. K. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, J. Vandewalle, Least Squares Support Vector Machines, World Scientific Pub. Co., Singapore, 2002. ISBN 981-238-151-1 Suykens J. A. K., Vandewalle J., Least squares support vector machine classifiers, Neural Processing L... |
Least-squares support vector machine : www.esat.kuleuven.be/sista/lssvmlab/ "Least squares support vector machine Lab (LS-SVMlab) toolbox contains Matlab/C implementations for a number of LS-SVM algorithms". www.kernel-machines.org "Support Vector Machines and Kernel based methods (Smola & Schölkopf)". www.gaussianproc... |
Margin (machine learning) : In machine learning, the margin of a single data point is defined to be the distance from the data point to a decision boundary. Note that there are many distances and decision boundaries that may be appropriate for certain datasets and goals. A margin classifier is a classification model th... |
Radial basis function kernel : In machine learning, the radial basis function kernel, or RBF kernel, is a popular kernel function used in various kernelized learning algorithms. In particular, it is commonly used in support vector machine classification. The RBF kernel on two samples x ∈ R k \in \mathbb ^ and x ′ ,... |
Radial basis function kernel : Because support vector machines and other models employing the kernel trick do not scale well to large numbers of training samples or large numbers of features in the input space, several approximations to the RBF kernel (and similar kernels) have been introduced. Typically, these take th... |
Radial basis function kernel : Gaussian function Kernel (statistics) Polynomial kernel Radial basis function Radial basis function network Obst kernel network == References == |
Ranking SVM : In machine learning, a ranking SVM is a variant of the support vector machine algorithm, which is used to solve certain ranking problems (via learning to rank). The ranking SVM algorithm was published by Thorsten Joachims in 2002. The original purpose of the algorithm was to improve the performance of an ... |
Ranking SVM : The ranking SVM algorithm is a learning retrieval function that employs pairwise ranking methods to adaptively sort results based on how 'relevant' they are for a specific query. The ranking SVM function uses a mapping function to describe the match between a search query and the features of each of the p... |
Ranking SVM : Ranking SVM can be applied to rank the pages according to the query. The algorithm can be trained using click-through data, where consists of the following three parts: Query. Present ranking of search results Search results clicked on by user The combination of 2 and 3 cannot provide full training data o... |
Regularization perspectives on support vector machines : Within mathematical analysis, Regularization perspectives on support-vector machines provide a way of interpreting support-vector machines (SVMs) in the context of other regularization-based machine-learning algorithms. SVM algorithms categorize binary data, with... |
Regularization perspectives on support vector machines : In the statistical learning theory framework, an algorithm is a strategy for choosing a function f : X → Y \to \mathbf given a training set S = ,y_),\ldots ,(x_,y_)\ of inputs x i and their labels y i (the labels are usually ± 1 ). Regularization strategie... |
Regularization perspectives on support vector machines : The simplest and most intuitive loss function for categorization is the misclassification loss, or 0–1 loss, which is 0 if f ( x i ) = y i )=y_ and 1 if f ( x i ) ≠ y i )\neq y_ , i.e. the Heaviside step function on − y i f ( x i ) f(x_) . However, this loss func... |
Regularization perspectives on support vector machines : The Tikhonov regularization problem can be shown to be equivalent to traditional formulations of SVM by expressing it in terms of the hinge loss. With the hinge loss V ( y i , f ( x i ) ) = ( 1 − y f ( x ) ) + , y_,f(x_)=1-yf(x)_, where ( s ) + = max ( s , 0 ) =\... |
Regularization perspectives on support vector machines : Evgeniou, Theodoros; Massimiliano Pontil; Tomaso Poggio (2000). "Regularization Networks and Support Vector Machines" (PDF). Advances in Computational Mathematics. 13 (1): 1–50. doi:10.1023/A:1018946025316. S2CID 70866. Joachims, Thorsten. "SVMlight". Archived fr... |
Sequential minimal optimization : Sequential minimal optimization (SMO) is an algorithm for solving the quadratic programming (QP) problem that arises during the training of support-vector machines (SVM). It was invented by John Platt in 1998 at Microsoft Research. SMO is widely used for training support vector machine... |
Sequential minimal optimization : Consider a binary classification problem with a dataset (x1, y1), ..., (xn, yn), where xi is an input vector and yi ∈ is a binary label corresponding to it. A soft-margin support vector machine is trained by solving a quadratic programming problem, which is expressed in the dual form ... |
Sequential minimal optimization : SMO is an iterative algorithm for solving the optimization problem described above. SMO breaks this problem into a series of smallest possible sub-problems, which are then solved analytically. Because of the linear equality constraint involving the Lagrange multipliers α i , the small... |
Sequential minimal optimization : The first approach to splitting large SVM learning problems into a series of smaller optimization tasks was proposed by Bernhard Boser, Isabelle Guyon, Vladimir Vapnik. It is known as the "chunking algorithm". The algorithm starts with a random subset of the data, solves this problem, ... |
Sequential minimal optimization : Kernel perceptron == References == |
Structured support vector machine : The structured support-vector machine is a machine learning algorithm that generalizes the Support-Vector Machine (SVM) classifier. Whereas the SVM classifier supports binary classification, multiclass classification and regression, the structured SVM allows training of a classifier ... |
Structured support vector machine : For a set of n training instances ( x i , y i ) ∈ X × Y _,y_)\in \times , i = 1 , … , n from a sample space X and label space Y , the structured SVM minimizes the following regularized risk function. min w ‖ w ‖ 2 + C ∑ i = 1 n max y ∈ Y ( 0 , Δ ( y i , y ) + ⟨ w , Ψ ( x i , y )... |
Structured support vector machine : At test time, only a sample x ∈ X \in is known, and a prediction function f : X → Y \to maps it to a predicted label from the label space Y . For structured SVMs, given the vector w obtained from training, the prediction function is the following. f ( x ) = argmax y ∈ Y ⟨ w , Ψ (... |
Structured support vector machine : The above quadratic program involves a very large, possibly infinite number of linear inequality constraints. In general, the number of inequalities is too large to be optimized over explicitly. Instead the problem is solved by using delayed constraint generation where only a finite ... |
Structured support vector machine : Ioannis Tsochantaridis, Thorsten Joachims, Thomas Hofmann and Yasemin Altun (2005), Large Margin Methods for Structured and Interdependent Output Variables, JMLR, Vol. 6, pages 1453-1484. Thomas Finley and Thorsten Joachims (2008), Training Structural SVMs when Exact Inference is Int... |
Supervised learning : In machine learning, supervised learning (SL) is a paradigm where a model is trained using input objects (e.g. a vector of predictor variables) and desired output values (also known as a supervisory signal), which are often human-made labels. The training process builds a function that maps new da... |
Supervised learning : To solve a given problem of supervised learning, the following steps must be performed: Determine the type of training samples. Before doing anything else, the user should decide what kind of data is to be used as a training set. In the case of handwriting analysis, for example, this might be a si... |
Supervised learning : A wide range of supervised learning algorithms are available, each with its strengths and weaknesses. There is no single learning algorithm that works best on all supervised learning problems (see the No free lunch theorem). There are four major issues to consider in supervised learning: |
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