Eguchi Shinto (28 results)
- Softcover
Seller: Books From California, Simi Valley, CA, U.S.A.Books From California
Contact seller4-star sellerCondition: Used - Very good
£ 24.86
£ 3.69 shippingShips within U.S.A.Quantity: 1 available
paperback. Condition: Very Good. Cover and edges may have some wear.
- Softcover
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
Contact seller5-star sellerCondition: New
£ 52.97
Free ShippingShips within U.S.A.Quantity: 1 available
Paperback. Condition: new. Paperback. This book introduces the gamma-divergence, a measure of distance between probability distributions that was proposed by Fujisawa and Eguchi in 2008. The gamma-divergence has been extensively explored to provide robust estimation when the power index g is positive. The gamma-divergence can be… defined even when the power index g is negative, as long as the condition of integrability is satisfied. Thus, the authors consider the gamma-divergence defined on a set of discrete distributions. The arithmetic, geometric, and harmonic means for the distribution ratios are closely connected with the gamma-divergence with a negative g. In particular, the authors call the geometric-mean (GM) divergence the gamma-divergence when g is equal to -1.The book begins by providing an overview of the gamma-divergence and its properties. It then goes on to discuss the applications of the gamma-divergence in various areas, including machine learning, statistics, and ecology. Bernoulli, categorical, Poisson, negative binomial, and Boltzmann distributions are discussed as typical examples. Furthermore, regression analysis models that explicitly or implicitly assume these distributions as the dependent variable in generalized linear models are discussed to apply the minimum gamma-divergence method.In ensemble learning, AdaBoost is derived by the exponential loss function in the weighted majority vote manner. It is pointed out that the exponential loss function is deeply connected to the GM divergence. In the Boltzmann machine, the maximum likelihood has to use approximation methods such as mean field approximation because of the intractable computation of the partition function. However, by considering the GM divergence and the exponential loss, it is shown that the calculation of the partition function is not necessary, and it can be executed without variational inference. In particular, the authors call the geometric-mean (GM) divergence the gamma-divergence when g is equal to -1.The book begins by providing an overview of the gamma-divergence and its properties. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
- Softcover
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
Contact seller5-star sellerCondition: New
£ 44.22
£ 11.98 shippingShips from United Kingdom to U.S.A.Quantity: Over 20 available
Condition: New. In.
- Softcover
Seller: Books Puddle, New York, NY, U.S.A.Books Puddle
Contact seller4-star sellerCondition: New
£ 64.88
£ 2.95 shippingShips within U.S.A.Quantity: 4 available
Condition: New.
- Softcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
Contact seller5-star sellerCondition: New
£ 62.29
£ 10.00 shippingShips from United Kingdom to U.S.A.Quantity: 2 available
Paperback. Condition: Brand New. 1st edition. 59 pages. 9.25x6.25x0.50 inches. In Stock.
- Hardcover
Seller: StainesBook, Weybridge, SURRE, United KingdomStainesBook
Contact seller1-star sellerCondition: New
£ 70.53
£ 30.00 shippingShips from United Kingdom to U.S.A.Quantity: 1 available
Condition: New. A brand new book in pristine condition. Showing zero signs of shelf wear, creases, or damage.
- Softcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
Contact seller5-star sellerCondition: New
£ 51.46
£ 52.23 shippingShips from Germany to U.S.A.Quantity: 1 available
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book introduces the gamma-divergence, a measure of distance between probability distributions that was proposed by Fujisawa and Eguchi in 2008. The gamma-divergence has been extensively explored to provide robust estimation when the power inde…x Gamma is positive. The gamma-divergence can be defined even when the power index Gamma is negative, as long as the condition of integrability is satisfied. Thus, the authors consider the gamma-divergence defined on a set of discrete distributions. The arithmetic, geometric, and harmonic means for the distribution ratios are closely connected with the gamma-divergence with a negative Gamma. In particular, the authors call the geometric-mean (GM) divergence the gamma-divergence when Gamma is equal to -1.The book begins by providing an overview of the gamma-divergence and its properties. It then goes on to discuss the applications of the gamma-divergence in various areas, including machine learning, statistics, and ecology. Bernoulli, categorical, Poisson, negative binomial, and Boltzmann distributions are discussed as typical examples. Furthermore, regression analysis models that explicitly or implicitly assume these distributions as the dependent variable in generalized linear models are discussed to apply the minimum gamma-divergence method.In ensemble learning, AdaBoost is derived by the exponential loss function in the weighted majority vote manner. It is pointed out that the exponential loss function is deeply connected to the GM divergence. In the Boltzmann machine, the maximum likelihood has to use approximation methods such as mean field approximation because of the intractable computation of the partition function. However, by considering the GM divergence and the exponential loss, it is shown that the calculation of the partition function is not necessary, and it can be executed without variational inference.
- Softcover
Seller: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
Contact seller5-star sellerCondition: New
£ 82.23
£ 27.34 shippingShips from Australia to U.S.A.Quantity: 1 available
Paperback. Condition: new. Paperback. This book introduces the gamma-divergence, a measure of distance between probability distributions that was proposed by Fujisawa and Eguchi in 2008. The gamma-divergence has been extensively explored to provide robust estimation when the power index g is positive. The gamma-divergence can be… defined even when the power index g is negative, as long as the condition of integrability is satisfied. Thus, the authors consider the gamma-divergence defined on a set of discrete distributions. The arithmetic, geometric, and harmonic means for the distribution ratios are closely connected with the gamma-divergence with a negative g. In particular, the authors call the geometric-mean (GM) divergence the gamma-divergence when g is equal to -1.The book begins by providing an overview of the gamma-divergence and its properties. It then goes on to discuss the applications of the gamma-divergence in various areas, including machine learning, statistics, and ecology. Bernoulli, categorical, Poisson, negative binomial, and Boltzmann distributions are discussed as typical examples. Furthermore, regression analysis models that explicitly or implicitly assume these distributions as the dependent variable in generalized linear models are discussed to apply the minimum gamma-divergence method.In ensemble learning, AdaBoost is derived by the exponential loss function in the weighted majority vote manner. It is pointed out that the exponential loss function is deeply connected to the GM divergence. In the Boltzmann machine, the maximum likelihood has to use approximation methods such as mean field approximation because of the intractable computation of the partition function. However, by considering the GM divergence and the exponential loss, it is shown that the calculation of the partition function is not necessary, and it can be executed without variational inference. In particular, the authors call the geometric-mean (GM) divergence the gamma-divergence when g is equal to -1.The book begins by providing an overview of the gamma-divergence and its properties. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
- Softcover
Seller: Mispah books, Redhill, SURRE, United KingdomMispah books
Contact seller4-star sellerCondition: New
£ 92.00
£ 25.00 shippingShips from United Kingdom to U.S.A.Quantity: 1 available
Paperback. Condition: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
- Hardcover
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
Contact seller5-star sellerCondition: New
£ 117.36
£ 11.98 shippingShips from United Kingdom to U.S.A.Quantity: Over 20 available
Condition: New. In.
- Hardcover
Seller: California Books, Miami, FL, U.S.A.California Books
Contact seller4-star sellerCondition: New
£ 163.62
Free ShippingShips within U.S.A.Quantity: Over 20 available
Condition: New.
- Hardcover
Seller: Books Puddle, New York, NY, U.S.A.Books Puddle
Contact seller4-star sellerCondition: New
£ 180.95
£ 2.95 shippingShips within U.S.A.Quantity: 4 available
Condition: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.
Minimum Divergence Methods in Statistical Machine Learning : From an Information Geometric Viewpoint
- Hardcover
Seller: Buchpark, Trebbin, GermanyBuchpark
Contact seller5-star sellerCondition: Used
£ 93.59
£ 89.85 shippingShips from Germany to U.S.A.Quantity: 2 available
Condition: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | This book explores minimum divergence methods of statistical machine learning for estimation, regression, prediction, and so forth, in which we engage in information geometry to elucidate their intrinsic properties of the corresponding loss… functions, learning algorithms, and statistical models. One of the most elementary examples is Gauss's least squares estimator in a linear regression model, in which the estimator is given by minimization of the sum of squares between a response vector and a vector of the linear subspace hulled by explanatory vectors. This is extended to Fisher's maximum likelihood estimator (MLE) for an exponential model, in which the estimator is provided by minimization of the Kullback-Leibler (KL) divergence between a data distribution and a parametric distribution of the exponential model in an empirical analogue. Thus, we envisage a geometric interpretation of such minimization procedures such that a right triangle is kept with Pythagorean identity in the sense of the KL divergence. This understanding sublimates a dualistic interplay between a statistical estimation and model, which requires dual geodesic paths, called m-geodesic and e-geodesic paths, in a framework of information geometry. We extend such a dualistic structure of the MLE and exponential model to that of the minimum divergence estimator and the maximum entropy model, which is applied to robust statistics, maximum entropy, density estimation, principal component analysis, independent component analysis, regression analysis, manifold learning, boosting algorithm, clustering, dynamic treatment regimes, and so forth. We consider a variety of information divergence measures typically including KL divergence to express departure from one probability distribution to another. An information divergence is decomposed into the cross-entropy and the (diagonal) entropy in which the entropy associates with a generative model as a family of maximum entropy distributions; the cross entropy associates with a statistical estimation method via minimization of the empirical analogue based on given data. Thus any statistical divergence includes an intrinsic object between the generative model and the estimation method. Typically, KL divergence leads to the exponential model and the maximum likelihood estimation. It is shown that any information divergence leads to a Riemannian metric and a pair of the linear connections in the framework of information geometry. We focus on a class of information divergence generated by an increasing and convex function U, called U-divergence. It is shown that any generator function U generates the U-entropy and U-divergence, in which there is a dualistic structure between the U-divergence method and the maximum U-entropy model. We observe that a specific choice of U leads to a robust statistical procedurevia the minimum U-divergence method. If U is selected as an exponential function, then the corresponding U-entropy and U-divergence are reduced to the Boltzmann-Shanon entropy and the KL divergence; the minimum U-divergence estimator is equivalent to the MLE. For robust supervised learning to predict a class label we observe that the U-boosting algorithm performs well for contamination of mislabel examples if U is appropriately selected. We present such maximal U-entropy and minimum U-divergence methods, in particular, selecting a power function as U to provide flexible performance in statistical machine l.
- Hardcover
Seller: BUCHSERVICE / ANTIQUARIAT Lars Lutzer, Wahlstedt, GermanyBUCHSERVICE / ANTIQUARIAT Lars Lutzer
Contact seller5-star sellerCondition: Used - Very good
£ 166.59
£ 34.19 shippingShips from Germany to U.S.A.Quantity: 1 available
Hardcover. Condition: gut. 2022. Minimum Divergence Methods in Statistical Machine Learning In deutscher Sprache. pages.
Minimum Divergence Methods in Statistical Machine Learning : From an Information Geometric Viewpoint
- Hardcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
Contact seller5-star sellerCondition: New
£ 148.72
£ 53.56 shippingShips from Germany to U.S.A.Quantity: 1 available
Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book explores minimum divergence methods of statistical machine learning for estimation, regression, prediction, and so forth, in which we engage in information geometry to elucidate their intrinsic properties of the corresponding loss functions, lea…rning algorithms, and statistical models.One of the most elementary examples is Gauss's least squares estimator in a linear regression model, in which the estimator is given by minimization of the sum of squares between a response vector and a vector of the linear subspace hulled by explanatory vectors. This is extended to Fisher's maximum likelihood estimator (MLE) for an exponential model, in which the estimator is provided by minimization of the Kullback-Leibler (KL) divergence between a data distribution and a parametric distribution of the exponential model in an empirical analogue. Thus, we envisage a geometric interpretation of such minimization procedures such that a right triangle is kept with Pythagorean identity in the sense of the KL divergence. This understanding sublimates a dualistic interplay between a statistical estimation and model, which requires dual geodesic paths, called m-geodesic and e-geodesic paths, in a framework of information geometry. We extend such a dualistic structure of the MLE and exponential model to that of the minimum divergence estimator and the maximum entropy model, which is applied to robust statistics, maximum entropy, density estimation, principal component analysis, independent component analysis, regression analysis, manifold learning, boosting algorithm, clustering, dynamic treatment regimes, and so forth. We consider a variety of information divergence measures typically including KL divergence to express departure from one probability distribution to another. An information divergence is decomposed into the cross-entropy and the (diagonal) entropy in which the entropy associates with a generative model as a family of maximum entropy distributions; the cross entropy associates with a statistical estimation method via minimization of the empirical analogue based on given data. Thus any statistical divergence includes an intrinsic object between the generative model and the estimation method. Typically, KL divergence leads to the exponential model and the maximum likelihood estimation. It is shown that any information divergence leads to a Riemannian metric and a pair of the linear connections in the framework of information geometry. We focus on a class of information divergence generated by an increasing and convex function U, called U-divergence. It is shown that any generator function U generates the U-entropy and U-divergence, in which there is a dualistic structure between the U-divergence method and the maximum U-entropy model. We observe that a specific choice of U leads to a robust statistical procedurevia the minimum U-divergence method. If U is selected as an exponential function, then the corresponding U-entropy and U-divergence are reduced to the Boltzmann-Shanon entropy and the KL divergence; the minimum U-divergence estimator is equivalent to the MLE. For robust supervised learning to predict a class label we observe that the U-boosting algorithm performs well for contamination of mislabel examples if U is appropriately selected. We present such maximal U-entropy and minimum U-divergence methods, in particular, selecting a power function as U to provide flexible performance in statistical machine learning.
- Softcover
- Print on Demand
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
Contact seller5-star sellerCondition: New
£ 39.99
£ 10.00 shippingShips from United Kingdom to U.S.A.Quantity: 1 available
Paperback. Condition: Brand New. 126 pages. 9.25x6.10x9.21 inches. In Stock. This item is printed on demand.
Language: English
Published by Springer, Berlin, Springer Nature Singapore, Springer, 2025
- Softcover
- Print on Demand
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
Contact seller5-star sellerCondition: New
£ 42.43
£ 19.68 shippingShips from Germany to U.S.A.Quantity: 2 available
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book introduces the gamma-divergence, a measure of distance between probability distributions that was proposed by Fujisawa and Eguchi in 2008. The gamma-divergence has been extensively explored to provide robust estimation whe…n the power index Gamma is positive. The gamma-divergence can be defined even when the power index Gamma is negative, as long as the condition of integrability is satisfied. Thus, the authors consider the gamma-divergence defined on a set of discrete distributions. The arithmetic, geometric, and harmonic means for the distribution ratios are closely connected with the gamma-divergence with a negative Gamma. In particular, the authors call the geometric-mean (GM) divergence the gamma-divergence when Gamma is equal to -1.The book begins by providing an overview of the gamma-divergence and its properties. It then goes on to discuss the applications of the gamma-divergence in various areas, including machine learning, statistics, and ecology. Bernoulli, categorical, Poisson, negative binomial, and Boltzmann distributions are discussed as typical examples. Furthermore, regression analysis models that explicitly or implicitly assume these distributions as the dependent variable in generalized linear models are discussed to apply the minimum gamma-divergence method.In ensemble learning, AdaBoost is derived by the exponential loss function in the weighted majority vote manner. It is pointed out that the exponential loss function is deeply connected to the GM divergence. In the Boltzmann machine, the maximum likelihood has to use approximation methods such as mean field approximation because of the intractable computation of the partition function. However, by considering the GM divergence and the exponential loss, it is shown that the calculation of the partition function is not necessary, and it can be executed without variational inference. 118 pp. Englisch.
- Softcover
- Print on Demand
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
Contact seller5-star sellerCondition: New
£ 47.14
£ 19.68 shippingShips from Germany to U.S.A.Quantity: 2 available
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents a fresh, new approach in that it provides a comprehensive recent review of challenging problems caused by imbalanced data in prediction and classification, and also in that it introduces several of the latest stat…istical methods of dealing with these problems. The book discusses the property of the imbalance of data from two points of view. The first is quantitative imbalance, meaning that the sample size in one population highly outnumbers that in another population. It includes presence-only data as an extreme case, where the presence of a species is confirmed, whereas the information on its absence is uncertain, which is especially common in ecology in predicting habitat distribution. The second is qualitative imbalance, meaning that the data distribution of one population can be well specified whereas that of the other one shows a highly heterogeneous property. A typical case is the existence of outliers commonly observed in gene expression data, and another is heterogeneous characteristics often observed in a case group in case-control studies. The extension of the logistic regression model, maxent, and AdaBoost for imbalanced data is discussed, providing a new framework for improvement of prediction, classification, and performance of variable selection. Weights functions introduced in the methods play an important role in alleviating the imbalance of data. This book also furnishes a new perspective on these problem and shows some applications of the recently developed statistical methods to real data sets. 68 pp. Englisch.
- Softcover
- Print on Demand
Seller: Majestic Books, Hounslow, United KingdomMajestic Books
Contact seller4-star sellerCondition: New
£ 64.76
£ 6.50 shippingShips from United Kingdom to U.S.A.Quantity: 4 available
Condition: New. Print on Demand.
- Softcover
- Print on Demand
Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
Contact seller4-star sellerCondition: New
£ 68.71
£ 8.51 shippingShips from Germany to U.S.A.Quantity: 4 available
Condition: New. PRINT ON DEMAND.
- Softcover
- Print on Demand
Seller: moluna, Greven, Germanymoluna
Contact seller5-star sellerCondition: New
£ 42.63
£ 41.92 shippingShips from Germany to U.S.A.Quantity: Over 20 available
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.
- Softcover
- Print on Demand
Seller: moluna, Greven, Germanymoluna
Contact seller5-star sellerCondition: New
£ 42.63
£ 41.92 shippingShips from Germany to U.S.A.Quantity: Over 20 available
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Osamu Komori, The Institute of Statistical Mathematics, Shinto Eguchi, The Institute of Statistical MathematicsThis book presents a fresh, new approach in that it provides a comprehensive recent review of challeng…ing problems caused by imba.
- Softcover
- Print on Demand
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
Contact seller5-star sellerCondition: New
£ 47.14
£ 51.34 shippingShips from Germany to U.S.A.Quantity: 1 available
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book introduces the gamma-divergence, a measure of distance between probability distributions that was proposed by Fujisawa and Eguchi in 2008. The gamma-divergence has been extensively explored to provide robust estimation when th…e power index ¿ is positive. The gamma-divergence can be defined even when the power index ¿ is negative, as long as the condition of integrability is satisfied. Thus, the authors consider the gamma-divergence defined on a set of discrete distributions. The arithmetic, geometric, and harmonic means for the distribution ratios are closely connected with the gamma-divergence with a negative ¿. In particular, the authors call the geometric-mean (GM) divergence the gamma-divergence when ¿ is equal to -1.The book begins by providing an overview of the gamma-divergence and its properties. It then goes on to discuss the applications of the gamma-divergence in various areas, including machine learning, statistics, and ecology. Bernoulli, categorical, Poisson, negative binomial, and Boltzmann distributions are discussed as typical examples. Furthermore, regression analysis models that explicitly or implicitly assume these distributions as the dependent variable in generalized linear models are discussed to apply the minimum gamma-divergence method.In ensemble learning, AdaBoost is derived by the exponential loss function in the weighted majority vote manner. It is pointed out that the exponential loss function is deeply connected to the GM divergence. In the Boltzmann machine, the maximum likelihood has to use approximation methods such as mean field approximation because of the intractable computation of the partition function. However, by considering the GM divergence and the exponential loss, it is shown that the calculation of the partition function is not necessary, and it can be executed without variational inference.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 128 pp. Englisch.
- Hardcover
- Print on Demand
Seller: Brook Bookstore On Demand, Napoli, NA, ItalyBrook Bookstore On Demand
Contact seller5-star sellerCondition: New
£ 111.29
£ 5.82 shippingShips from Italy to U.S.A.Quantity: Over 20 available
Condition: new. Questo è un articolo print on demand.
- Hardcover
- Print on Demand
Seller: moluna, Greven, Germanymoluna
Contact seller5-star sellerCondition: New
£ 120.01
£ 41.92 shippingShips from Germany to U.S.A.Quantity: Over 20 available
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides various applications including boosting and kernel methods in machine learning with a geometric invariance viewpointFacilitates a deeper understanding of the complementary relation between statistical models…and estimation in the context .
- Hardcover
- Print on Demand
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
Contact seller5-star sellerCondition: New
£ 141.45
£ 51.34 shippingShips from Germany to U.S.A.Quantity: 1 available
Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book explores minimum divergence methods of statistical machine learning for estimation, regression, prediction, and so forth, in which we engage in information geometry to elucidate their intrinsic properties of the corresponding loss fu…nctions, learning algorithms, and statistical models. One of the most elementary examples is Gauss's least squares estimator in a linear regression model, in which the estimator is given by minimization of the sum of squares between a response vector and a vector of the linear subspace hulled by explanatory vectors. This is extended to Fisher's maximum likelihood estimator (MLE) for an exponential model, in which the estimator is provided by minimization of the Kullback-Leibler (KL) divergence between a data distribution and a parametric distribution of the exponential model in an empirical analogue. Thus, we envisage a geometric interpretation of such minimization procedures such that a right triangle is kept with Pythagorean identity in the sense of the KL divergence. This understanding sublimates a dualistic interplay between a statistical estimation and model, which requires dual geodesic paths, called m-geodesic and e-geodesic paths, in a framework of information geometry.We extend such a dualistic structure of the MLE and exponential model to that of the minimum divergence estimator and the maximum entropy model, which is applied to robust statistics, maximum entropy, density estimation, principal component analysis, independent component analysis, regression analysis, manifold learning, boosting algorithm, clustering, dynamic treatment regimes, and so forth. We consider a variety of information divergence measures typically including KL divergence to express departure from one probability distribution to another. An information divergence is decomposed into the cross-entropy and the (diagonal) entropy in which the entropy associates with a generative model as a family of maximum entropy distributions; the cross entropy associates with a statistical estimation method via minimization of the empirical analogue based on given data. Thus any statistical divergence includes an intrinsic object between the generative model and the estimation method. Typically, KL divergence leads to the exponential model and the maximum likelihood estimation. It is shown that any information divergence leads to a Riemannian metric and a pair of the linear connections in the framework of information geometry.We focus on a class of information divergence generated by an increasing and convex function U, called U-divergence. It is shown that any generator function U generates the U-entropy and U-divergence, in which there is a dualistic structure between the U-divergence method and the maximum U-entropy model. We observe that a specific choice of U leads to a robust statistical procedurevia the minimum U-divergence method. If U is selected as an exponential function, then the corresponding U-entropy and U-divergence are reduced to the Boltzmann-Shanon entropy and the KL divergence; the minimum U-divergence estimator is equivalent to the MLE. For robust supervised learning to predict a class label we observe that the U-boosting algorithm performs well for contamination of mislabel examples if U is appropriately selected. We present such maximal U-entropy and minimum U-divergence methods, in particular, selecting a power function as U to provide flexible performance in statistical machine learning.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 232 pp. Englisch.
- Hardcover
- Print on Demand
Seller: Majestic Books, Hounslow, United KingdomMajestic Books
Contact seller4-star sellerCondition: New
£ 189.16
£ 6.50 shippingShips from United Kingdom to U.S.A.Quantity: 4 available
Condition: New. Print on Demand.
- Hardcover
- Print on Demand
Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
Contact seller4-star sellerCondition: New
£ 199.62
£ 8.51 shippingShips from Germany to U.S.A.Quantity: 4 available
Condition: New. PRINT ON DEMAND.
