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Handwriting recognition : AI effect Applications of artificial intelligence Electronic signature eScriptorium Handwriting movement analysis Intelligent character recognition Live Ink Character Recognition Solution Neocognitron Optical character recognition Pen computing Sketch recognition Stylus (computing) Tablet PC |
Handwriting recognition : Annotated bibliography of references to gesture and pen computing Notes on the History of Pen-based Computing – video on YouTube |
Inverted pendulum : An inverted pendulum is a pendulum that has its center of mass above its pivot point. It is unstable and falls over without additional help. It can be suspended stably in this inverted position by using a control system to monitor the angle of the pole and move the pivot point horizontally back unde... |
Inverted pendulum : A pendulum with its bob hanging directly below the support pivot is at a stable equilibrium point, where it remains motionless because there is no torque on the pendulum. If displaced from this position, it experiences a restoring torque that returns it toward the equilibrium position. A pendulum wi... |
Inverted pendulum : The equations of motion of inverted pendulums are dependent on what constraints are placed on the motion of the pendulum. Inverted pendulums can be created in various configurations resulting in a number of Equations of Motion describing the behavior of the pendulum. |
Inverted pendulum : Achieving stability of an inverted pendulum has become a common engineering challenge for researchers. There are different variations of the inverted pendulum on a cart ranging from a rod on a cart to a multiple segmented inverted pendulum on a cart. Another variation places the inverted pendulum's ... |
Inverted pendulum : Arguably the most prevalent example of a stabilized inverted pendulum is a human being. A person standing upright acts as an inverted pendulum with their feet as the pivot, and without constant small muscular adjustments would fall over. The human nervous system contains an unconscious feedback cont... |
Inverted pendulum : Double inverted pendulum Inertia wheel pendulum Furuta pendulum iBOT Humanoid robot Ballbot |
Inverted pendulum : D. Liberzon Switching in Systems and Control (2003 Springer) pp. 89ff |
Inverted pendulum : Franklin; et al. (2005). Feedback control of dynamic systems, 5, Prentice Hall. ISBN 0-13-149930-0 |
Inverted pendulum : YouTube - Inverted Pendulum - Demo #3 YouTube - inverted pendulum YouTube - Double Pendulum on a Cart YouTube - Triple Pendulum on a Cart A dynamical simulation of an inverse pendulum on an oscillatory base Archived 2019-09-13 at the Wayback Machine Inverted Pendulum: Analysis, Design, and Implement... |
Speech recognition : Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers. It is also known as automatic speech recognition (ASR), computer ... |
Speech recognition : The key areas of growth were: vocabulary size, speaker independence, and processing speed. |
Speech recognition : Both acoustic modeling and language modeling are important parts of modern statistically based speech recognition algorithms. Hidden Markov models (HMMs) are widely used in many systems. Language modeling is also used in many other natural language processing applications such as document classific... |
Speech recognition : The performance of speech recognition systems is usually evaluated in terms of accuracy and speed. Accuracy is usually rated with word error rate (WER), whereas speed is measured with the real time factor. Other measures of accuracy include Single Word Error Rate (SWER) and Command Success Rate (CS... |
Speech recognition : Cole, Ronald; Mariani, Joseph; Uszkoreit, Hans; Varile, Giovanni Battista; Zaenen, Annie; Zampolli; Zue, Victor, eds. (1997). Survey of the state of the art in human language technology. Cambridge Studies in Natural Language Processing. Vol. XII–XIII. Cambridge University Press. ISBN 978-0-521-5927... |
Abess : abess (Adaptive Best Subset Selection, also ABESS) is a machine learning method designed to address the problem of best subset selection. It aims to determine which features or variables are crucial for optimal model performance when provided with a dataset and a prediction task. abess was introduced by Zhu in ... |
Abess : The basic form of abess is employed to address the optimal subset selection problem in general linear regression. abess is an l 0 method, it is characterized by its polynomial time complexity and the property of providing both unbiased and consistent estimates. In the context of linear regression, assuming we ... |
Abess : The splicing algorithm in abess can be employed for subset selection in other models. |
Abess : The abess library. (version 0.4.5) is an R package and python package based on C++ algorithms. It is open-source on GitHub. The library can be used for optimal subset selection in linear regression, (multi-)classification, and censored-response modeling models. The abess package allows for parameters to be chos... |
Abess : abess can be applied in biostatistics, such as assessing the robust severity of COVID-19 patients, conducting antibiotic resistance in Mycobacterium tuberculosis, exploring prognostic factors in neck pain, and developing prediction models for severe pain in patients after percutaneous nephrolithotomy. abess can... |
Accumulated local effects : Accumulated local effects (ALE) is a machine learning interpretability method. |
Accumulated local effects : ALE uses a conditional feature distribution as an input and generates augmented data, creating more realistic data than a marginal distribution. It ignores far out-of-distribution (outlier) values. Unlike partial dependence plots and marginal plots, ALE is not defeated in the presence of cor... |
Accumulated local effects : Given a model that predicts house prices based on its distance from city center and size of the building area, ALE compares the differences of predictions of houses of different sizes. The result separates the impact of the size from otherwise correlated features. |
Accumulated local effects : Defining evaluation windows is subjective. High correlations between features can defeat the technique. ALE requires more and more uniformly distributed observations than PDP so that the conditional distribution can be reliably determined. The technique may produce inadequate results if the ... |
Accumulated local effects : Interpretability (machine learning) |
Accumulated local effects : Munn, Michael (2022). Explainable AI for Practitioners. O'Reilly Media, Incorporated. ISBN 978-1-0981-1910-2. OCLC 1350433516. |
Algorithms of Oppression : Algorithms of Oppression: How Search Engines Reinforce Racism is a 2018 book by Safiya Umoja Noble in the fields of information science, machine learning, and human-computer interaction. |
Algorithms of Oppression : Noble earned an undergraduate degree in sociology from California State University, Fresno in the 1990s, then worked in advertising and marketing for fifteen years before going to the University of Illinois Urbana-Champaign for a Master of Library and Information Science degree in the early 2... |
Algorithms of Oppression : Algorithms of Oppression addresses the relationship between search engines and discriminatory biases. She takes a Black intersectional feminist approach. Intersectional feminism takes into account the experiences of women of different races and sexualities when discussing the oppression of wo... |
Algorithms of Oppression : Chapter 1 explores how Google search's auto suggestion feature is demoralizing, discussing example searches for terms like "black girls" (which returned pornography) and "Jew" (which returned anti-Semitic pages). Noble coins the term algorithmic oppression to describe data failures specific t... |
Algorithms of Oppression : Critical reception for Algorithms of Oppression has been largely positive. In the Los Angeles Review of Books, Emily Drabinski writes, "What emerges from these pages is the sense that Google’s algorithms of oppression comprise just one of the hidden infrastructures that govern our daily lives... |
Algorithms of Oppression : Algorithmic bias Techlash |
Algorithms of Oppression : Algorithms of Oppression: How Search Engines Reinforce Racism |
Almeida–Pineda recurrent backpropagation : Almeida–Pineda recurrent backpropagation is an extension to the backpropagation algorithm that is applicable to recurrent neural networks. It is a type of supervised learning. It was described somewhat cryptically in Richard Feynman's senior thesis, and rediscovered independen... |
Bootstrap aggregating : Bootstrap aggregating, also called bagging (from bootstrap aggregating) or bootstrapping, is a machine learning (ML) ensemble meta-algorithm designed to improve the stability and accuracy of ML classification and regression algorithms. It also reduces variance and overfitting. Although it is usu... |
Bootstrap aggregating : Given a standard training set D of size n , bagging generates m new training sets D i , each of size n ′ , by sampling from D uniformly and with replacement. By sampling with replacement, some observations may be repeated in each D i . If n ′ = n , then for large n the set D i is expec... |
Bootstrap aggregating : While the techniques described above utilize random forests and bagging (otherwise known as bootstrapping), there are certain techniques that can be used in order to improve their execution and voting time, their prediction accuracy, and their overall performance. The following are key steps in ... |
Bootstrap aggregating : For classification, use a training set D , Inducer I and the number of bootstrap samples m as input. Generate a classifier C ∗ as output Create m new training sets D i , from D with replacement Classifier C i is built from each set D i using I to determine the classification of set D i... |
Bootstrap aggregating : To illustrate the basic principles of bagging, below is an analysis on the relationship between ozone and temperature (data from Rousseeuw and Leroy (1986), analysis done in R). The relationship between temperature and ozone appears to be nonlinear in this dataset, based on the scatter plot. To ... |
Bootstrap aggregating : Advantages: Many weak learners aggregated typically outperform a single learner over the entire set, and have less overfit Reduces variance in high-variance low-bias weak learner, which can improve efficiency (statistics) Can be performed in parallel, as each separate bootstrap can be processed ... |
Bootstrap aggregating : The concept of bootstrap aggregating is derived from the concept of bootstrapping which was developed by Bradley Efron. Bootstrap aggregating was proposed by Leo Breiman who also coined the abbreviated term "bagging" (bootstrap aggregating). Breiman developed the concept of bagging in 1994 to im... |
Bootstrap aggregating : Boosting (machine learning) Bootstrapping (statistics) Cross-validation (statistics) Out-of-bag error Random forest Random subspace method (attribute bagging) Resampled efficient frontier Predictive analysis: Classification and regression trees |
Bootstrap aggregating : Breiman, Leo (1996). "Bagging predictors". Machine Learning. 24 (2): 123–140. CiteSeerX 10.1.1.32.9399. doi:10.1007/BF00058655. S2CID 47328136. Alfaro, E., Gámez, M. and García, N. (2012). "adabag: An R package for classification with AdaBoost.M1, AdaBoost-SAMME and Bagging". : Cite journal requ... |
Characteristic samples : Characteristic samples is a concept in the field of grammatical inference, related to passive learning. In passive learning, an inference algorithm I is given a set of pairs of strings and labels S , and returns a representation R that is consistent with S . Characteristic samples consider ... |
Characteristic samples : There are some classes that do not have polynomially sized characteristic samples. For example, from the first theorem in the Related theorems segment, it has been shown that the following classes of languages do not have polynomial sized characteristic samples: C F G - The class of context-f... |
Characteristic samples : Classes of representations that has characteristic samples relates to the following learning paradigms: |
Characteristic samples : Grammar induction Passive learning Induction of regular languages Deterministic finite automaton == References == |
Constructing skill trees : Constructing skill trees (CST) is a hierarchical reinforcement learning algorithm which can build skill trees from a set of sample solution trajectories obtained from demonstration. CST uses an incremental MAP (maximum a posteriori) change point detection algorithm to segment each demonstrati... |
Constructing skill trees : CST consists of mainly three parts;change point detection, alignment and merging. The main focus of CST is online change-point detection. The change-point detection algorithm is used to segment data into skills and uses the sum of discounted reward R t as the target regression variable. Each... |
Constructing skill trees : The following pseudocode describes the change point detection algorithm: particles := []; Process each incoming data point for t = 1:T do //Compute fit probabilities for all particles for p ∈ particles do p_tjq := (1 − G(t − p.pos − 1)) × p.fit_prob × model_prior(p.model) × p.prev_MAP p.MAP :... |
Constructing skill trees : CTS assume that the demonstrated skills form a tree, the domain reward function is known and the best model for merging a pair of skills is the model selected for representing both individually. |
Constructing skill trees : CST is much faster learning algorithm than skill chaining. CST can be applied to learning higher dimensional policies. Even unsuccessful episode can improve skills. Skills acquired using agent-centric features can be used for other problems. |
Constructing skill trees : CST has been used to acquire skills from human demonstration in the PinBall domain. It has been also used to acquire skills from human demonstration on a mobile manipulator. |
Constructing skill trees : Prefrontal cortex basal ganglia working memory State–action–reward–state–action Sammon Mapping |
Constructing skill trees : Konidaris, George; Scott Kuindersma; Andrew Barto; Roderic Grupen (2010). "Constructing Skill Trees for Reinforcement Learning Agents from Demonstration Trajectories". Advances in Neural Information Processing Systems 23. Konidaris, George; Andrew Barto (2009). "Skill discovery in continuous ... |
Curriculum learning : Curriculum learning is a technique in machine learning in which a model is trained on examples of increasing difficulty, where the definition of "difficulty" may be provided externally or discovered as part of the training process. This is intended to attain good performance more quickly, or to co... |
Curriculum learning : Most generally, curriculum learning is the technique of successively increasing the difficulty of examples in the training set that is presented to a model over multiple training iterations. This can produce better results than exposing the model to the full training set immediately under some cir... |
Curriculum learning : The term "curriculum learning" was introduced by Yoshua Bengio et al in 2009, with reference to the psychological technique of shaping in animals and structured education for humans: beginning with the simplest concepts and then building on them. The authors also note that the application of this ... |
Curriculum learning : Curriculum Learning: A Survey A Survey on Curriculum Learning Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey Curriculum learning at IEEE Xplore |
Diffusion map : Diffusion maps is a dimensionality reduction or feature extraction algorithm introduced by Coifman and Lafon which computes a family of embeddings of a data set into Euclidean space (often low-dimensional) whose coordinates can be computed from the eigenvectors and eigenvalues of a diffusion operator on... |
Diffusion map : Following and, diffusion maps can be defined in four steps. |
Diffusion map : The basic algorithm framework of diffusion map is as: Step 1. Given the similarity matrix L. Step 2. Normalize the matrix according to parameter α : L ( α ) = D − α L D − α =D^LD^ . Step 3. Form the normalized matrix M = ( D ( α ) ) − 1 L ( α ) ^)^L^ . Step 4. Compute the k largest eigenvalues of M t ... |
Diffusion map : In the paper Nadler et al. showed how to design a kernel that reproduces the diffusion induced by a Fokker–Planck equation. They also explained that, when the data approximate a manifold, one can recover the geometry of this manifold by computing an approximation of the Laplace–Beltrami operator. This c... |
Diffusion map : Nonlinear dimensionality reduction Spectral clustering == References == |
Dominance-based rough set approach : The dominance-based rough set approach (DRSA) is an extension of rough set theory for multi-criteria decision analysis (MCDA), introduced by Greco, Matarazzo and Słowiński. The main change compared to the classical rough sets is the substitution for the indiscernibility relation by ... |
Dominance-based rough set approach : Multicriteria classification (sorting) is one of the problems considered within MCDA and can be stated as follows: given a set of objects evaluated by a set of criteria (attributes with preference-order domains), assign these objects to some pre-defined and preference-ordered decisi... |
Dominance-based rough set approach : On the basis of the approximations obtained by means of the dominance relations, it is possible to induce a generalized description of the preferential information contained in the decision table, in terms of decision rules. The decision rules are expressions of the form if [conditi... |
Dominance-based rough set approach : Consider the following problem of high school students’ evaluations: Each object (student) is described by three criteria q 1 , q 2 , q 3 ,q_,q_\,\! , related to the levels in Mathematics, Physics and Literature, respectively. According to the decision attribute, the students are di... |
Dominance-based rough set approach : 4eMka2 Archived 2007-09-09 at the Wayback Machine is a decision support system for multiple criteria classification problems based on dominance-based rough sets (DRSA). JAMM Archived 2007-07-19 at the Wayback Machine is a much more advanced successor of 4eMka2. Both systems are free... |
Dominance-based rough set approach : Rough sets Granular computing Multicriteria Decision Analysis (MCDA) |
Dominance-based rough set approach : Chakhar S., Ishizaka A., Labib A., Saad I. (2016). Dominance-based rough set approach for group decisions, European Journal of Operational Research, 251(1): 206-224 Li S., Li T. Zhang Z., Chen H., Zhang J. (2015). Parallel Computing of Approximations in Dominance-based Rough Sets Ap... |
Dominance-based rough set approach : The International Rough Set Society Laboratory of Intelligent Decision Support Systems (IDSS) at Poznań University of Technology. Extensive list of DRSA references on the Roman Słowiński home page. |
Dynamic time warping : In time series analysis, dynamic time warping (DTW) is an algorithm for measuring similarity between two temporal sequences, which may vary in speed. For instance, similarities in walking could be detected using DTW, even if one person was walking faster than the other, or if there were accelerat... |
Dynamic time warping : This example illustrates the implementation of the dynamic time warping algorithm when the two sequences s and t are strings of discrete symbols. For two symbols x and y, d(x, y) is a distance between the symbols, e.g. d(x, y) = | x − y | . int DTWDistance(s: array [1..n], t: array [1..m]) wher... |
Dynamic time warping : The DTW algorithm produces a discrete matching between existing elements of one series to another. In other words, it does not allow time-scaling of segments within the sequence. Other methods allow continuous warping. For example, Correlation Optimized Warping (COW) divides the sequence into uni... |
Dynamic time warping : The time complexity of the DTW algorithm is O ( N M ) , where N and M are the lengths of the two input sequences. The 50 years old quadratic time bound was broken in 2016: an algorithm due to Gold and Sharir enables computing DTW in O ( N 2 / log log N ) /\log \log N) time and space for tw... |
Dynamic time warping : Fast techniques for computing DTW include PrunedDTW, SparseDTW, FastDTW, and the MultiscaleDTW. A common task, retrieval of similar time series, can be accelerated by using lower bounds such as LB_Keogh, LB_Improved, or LB_Petitjean. However, the Early Abandon and Pruned DTW algorithm reduces the... |
Dynamic time warping : Averaging for dynamic time warping is the problem of finding an average sequence for a set of sequences. NLAAF is an exact method to average two sequences using DTW. For more than two sequences, the problem is related to the one of the multiple alignment and requires heuristics. DBA is currently ... |
Dynamic time warping : A nearest-neighbour classifier can achieve state-of-the-art performance when using dynamic time warping as a distance measure. |
Dynamic time warping : Amerced Dynamic Time Warping (ADTW) is a variant of DTW designed to better control DTW's permissiveness in the alignments that it allows. The windows that classical DTW uses to constrain alignments introduce a step function. Any warping of the path is allowed within the window and none beyond it.... |
Dynamic time warping : In functional data analysis, time series are regarded as discretizations of smooth (differentiable) functions of time. By viewing the observed samples at smooth functions, one can utilize continuous mathematics for analyzing data. Smoothness and monotonicity of time warp functions may be obtained... |
Dynamic time warping : The tempo C++ library with Python bindings implements Early Abandoned and Pruned DTW as well as Early Abandoned and Pruned ADTW and DTW lower bounds LB_Keogh, LB_Enhanced and LB_Webb. The UltraFastMPSearch Java library implements the UltraFastWWSearch algorithm for fast warping window tuning. The... |
Dynamic time warping : Levenshtein distance Elastic matching Sequence alignment Multiple sequence alignment Wagner–Fischer algorithm Needleman–Wunsch algorithm Fréchet distance Nonlinear mixed-effects model |
Dynamic time warping : Pavel Senin, Dynamic Time Warping Algorithm Review Vintsyuk, T. K. (1968). "Speech discrimination by dynamic programming". Kibernetika. 4: 81–88. Sakoe, H.; Chiba (1978). "Dynamic programming algorithm optimization for spoken word recognition". IEEE Transactions on Acoustics, Speech, and Signal P... |
Elastic net regularization : In statistics and, in particular, in the fitting of linear or logistic regression models, the elastic net is a regularized regression method that linearly combines the L1 and L2 penalties of the lasso and ridge methods. Nevertheless, elastic net regularization is typically more accurate tha... |
Elastic net regularization : The elastic net method overcomes the limitations of the LASSO (least absolute shrinkage and selection operator) method which uses a penalty function based on ‖ β ‖ 1 = ∑ j = 1 p | β j | . =\textstyle \sum _^|\beta _|. Use of this penalty function has several limitations. For example, in the... |
Elastic net regularization : It was proven in 2014 that the elastic net can be reduced to the linear support vector machine. A similar reduction was previously proven for the LASSO in 2014. The authors showed that for every instance of the elastic net, an artificial binary classification problem can be constructed such... |
Elastic net regularization : "Glmnet: Lasso and elastic-net regularized generalized linear models" is a software which is implemented as an R source package and as a MATLAB toolbox. This includes fast algorithms for estimation of generalized linear models with ℓ1 (the lasso), ℓ2 (ridge regression) and mixtures of the t... |
Elastic net regularization : Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome (2017). "Shrinkage Methods" (PDF). The Elements of Statistical Learning : Data Mining, Inference, and Prediction (2nd ed.). New York: Springer. pp. 61–79. ISBN 978-0-387-84857-0. |
Elastic net regularization : Regularization and Variable Selection via the Elastic Net (presentation) |
Error-driven learning : In reinforcement learning, error-driven learning is a method for adjusting a model's (intelligent agent's) parameters based on the difference between its output results and the ground truth. These models stand out as they depend on environmental feedback, rather than explicit labels or categorie... |
Error-driven learning : Error-driven learning models are ones that rely on the feedback of prediction errors to adjust the expectations or parameters of a model. The key components of error-driven learning include the following: A set S of states representing the different situations that the learner can encounter. A ... |
Error-driven learning : Error-driven learning algorithms refer to a category of reinforcement learning algorithms that leverage the disparity between the real output and the expected output of a system to regulate the system's parameters. Typically applied in supervised learning, these algorithms are provided with a co... |
Error-driven learning : Error-driven learning has several advantages over other types of machine learning algorithms: They can learn from feedback and correct their mistakes, which makes them adaptive and robust to noise and changes in the data. They can handle large and high-dimensional data sets, as they do not requi... |
Error-driven learning : Although error driven learning has its advantages, their algorithms also have the following limitations: They can suffer from overfitting, which means that they memorize the training data and fail to generalize to new and unseen data. This can be mitigated by using regularization techniques, suc... |
Error-driven learning : Predictive coding == References == |
Evolutionary multimodal optimization : In applied mathematics, multimodal optimization deals with optimization tasks that involve finding all or most of the multiple (at least locally optimal) solutions of a problem, as opposed to a single best solution. Evolutionary multimodal optimization is a branch of evolutionary ... |
Evolutionary multimodal optimization : Knowledge of multiple solutions to an optimization task is especially helpful in engineering, when due to physical (and/or cost) constraints, the best results may not always be realizable. In such a scenario, if multiple solutions (locally and/or globally optimal) are known, the i... |
Evolutionary multimodal optimization : Classical techniques of optimization would need multiple restart points and multiple runs in the hope that a different solution may be discovered every run, with no guarantee however. Evolutionary algorithms (EAs) due to their population based approach, provide a natural advantage... |
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