sparsity - 三民網路書店

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2020~2021 （4）

2018~2019 （1）

2016~2017 （3）

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作者

Dinh Dũng （1）

Gang Li （1）

Jaroslav Nesetril/ Patrice Ossona De Mendez （1）

John Wright （1）

Masaaki Nagahara （1）

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Cambridge Univ Pr （4）

Springer Nature （3）

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共17筆商品，1/1頁

滿額折

Statistical Learning With Sparsity ─ The Lasso and Generalizations

作者：Trevor Hastie; Robert Tibshirani; Martin Wainwright 出版社：Productivity Press 出版日：2015/05/07 裝訂：精裝

Discover New Methods for Dealing with High-Dimensional Data A sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret

定價：6300 元，優惠價：1 6300

庫存：1

90折

Sparsity—Graphs, Structures, and Algorithms

作者：Jaroslav Nesetril; Patrice Ossona De Mendez 出版社：Springer Verlag 出版日：2012/04/24 裝訂：精裝

This is the first book devoted to the systematic study of sparse graphs and sparse finite structures. Although the notion of sparsity appears in various contexts and is a typical example of a hard to

定價：3750 元，優惠價：9 3375

無庫存，下單後進貨(到貨天數約45天)

Algorithms for Sparsity-Constrained Optimization

作者：Sohail Bahmani 出版社：Springer Verlag 出版日：2013/10/31 裝訂：精裝

This thesis presents a wholly new technique in the structural analysis of data that uses a ‘greedy’ algorithm to derive optimal sparse solutions, enabling faster and more accurate results in formerly

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Sparsity Methods for Systems and Control

作者：Masaaki Nagahara 出版社：NEW PUBL INC 出版日：2020/09/30 裝訂：精裝

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Advanced Sparsity-Driven Models and Methods for Radar Applications

作者：Gang Li 出版社：SCITECH PUB 出版日：2021/02/25 裝訂：精裝

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Study on Signal Detection and Recovery Methods with Joint Sparsity

作者：Xueqian Wang 出版社：Springer Nature 出版日：2023/10/17 裝訂：精裝

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90折

Statistical Learning with Sparsity: The Lasso and Generalizations

作者：Trevor Hastie 出版社：CRC PR INC 出版日：2020/12/20 裝訂：平裝

定價：3022 元，優惠價：9 2720

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Sparse Sensing and Sparsity Sensed in Multi-Sensor Array Applications

作者：Xiangrong Wang 出版社：Springer Nature 出版日：2024/03/14 裝訂：精裝

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90折

Estimation and Testing Under Sparsity ― Ecole D'ete De Probabilites De Saint-flour Xlv - 2015

作者：Sara Van De Geer 出版社：Springer Verlag 出版日：2016/06/29 裝訂：平裝

Taking the Lasso method as its starting point, this book describes the main ingredients needed to study general loss functions and sparsity-inducing regularizers. It also provides a semi-parametric ap

定價：2700 元，優惠價：9 2430

無庫存，下單後進貨(到貨天數約30-45天)

10.

Analyticity and Sparsity in Uncertainty Quantification for Pdes with Gaussian Random Field Inputs

作者：Dinh Dũng 出版社：Springer Nature 出版日：2023/09/22 裝訂：平裝

定價：2925 元，優惠價：1 2925

無庫存，下單後進貨(到貨天數約30-45天)

滿額折

11.

電子商務推薦系統瓶頸問題研究（簡體書）

作者：李聰 出版社：科學出版社 出版日：2017/04/10 裝訂：平裝

電子商務推薦系統是解決資訊超載的重要技術。協同過濾作為推薦系統中廣泛使用的、最成功的推薦演算法，還存在諸如稀疏性（sparsity）、冷開機（cold-start）、可擴展性（scalability）等制約其進一步發展的瓶頸問題。本書針對稀疏性問題，提出了非目標使用者類型區分理論、領域最近鄰理論、基於Rough 集理論的用戶評分項並集未評分值填補方法等；針對冷開機問題，提出了一種冷開機消除方

定價：528 元，優惠價：87 459

海外經銷商無庫存，到貨日平均30天至45天

12.

Computational Methods for Large Sparse Power Systems Analysis ― An Object Oriented Approach

作者：Shreevardhan Arunchandra Soman; S. A. Khaparde; Shubha Pandit 出版社：Springer Verlag 出版日：2013/10/03 裝訂：平裝

Computational methods in Power Systems require significant inputs from diverse disciplines, such as data base structures, numerical analysis etc. Strategic decisions in sparsity exploitation and algor

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13.

Perspectives in Shape Analysis

作者：Michael Breus (EDT); Alfred Bruckstein (EDT); Petros Maragos (EDT); Stefanie Wuhrer (EDT) 出版社：Springer Verlag 出版日：2016/10/01 裝訂：精裝

This book presents recent advances in the field of shape analysis. Written by experts in the fields of continuous-scale shape analysis, discrete shape analysis and sparsity, and numerical computing wh

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14.

Methods of Applied Mathematics for Engineers and Scientists ― Analytical and Numerical Approaches

作者：Tomas B. Co 出版社：Cambridge Univ Pr 出版日：2013/06/28 裝訂：精裝

Based on course notes from over twenty years of teaching engineering and physical sciences at Michigan Technological University, Tomas Co's engineering mathematics textbook is rich with examples, applications and exercises. Professor Co uses analytical approaches to solve smaller problems to provide mathematical insight and understanding, and numerical methods for large and complex problems. The book emphasises applying matrices with strong attention to matrix structure and computational issues such as sparsity and efficiency. Chapters on vector calculus and integral theorems are used to build coordinate-free physical models with special emphasis on orthogonal co-ordinates. Chapters on ODEs and PDEs cover both analytical and numerical approaches. Topics on analytical solutions include similarity transform methods, direct formulas for series solutions, bifurcation analysis, Lagrange–Charpit formulas, shocks/rarefaction and others. Topics on numerical methods include stability analysis,

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An Introduction to Sparse Stochastic Processes

滿額折

15.

An Introduction to Sparse Stochastic Processes

作者：Michael Unser 出版社：Cambridge Univ Pr 出版日：2014/10/31 裝訂：精裝

Providing a novel approach to sparsity, this comprehensive book presents the theory of stochastic processes that are ruled by linear stochastic differential equations, and that admit a parsimonious representation in a matched wavelet-like basis. Two key themes are the statistical property of infinite divisibility, which leads to two distinct types of behaviour - Gaussian and sparse - and the structural link between linear stochastic processes and spline functions, which is exploited to simplify the mathematical analysis. The core of the book is devoted to investigating sparse processes, including a complete description of their transform-domain statistics. The final part develops practical signal-processing algorithms that are based on these models, with special emphasis on biomedical image reconstruction. This is an ideal reference for graduate students and researchers with an interest in signal/image processing, compressed sensing, approximation theory, machine learning, or statistic

定價：2404 元，優惠價：9 2164

無庫存，下單後進貨(到貨天數約45-60天)

16.

Variational Bayesian Learning Theory

作者：Shinichi Nakajima 出版社：Cambridge Univ Pr 出版日：2019/08/31 裝訂：精裝

Variational Bayesian learning is one of the most popular methods in machine learning. Designed for researchers and graduate students in machine learning, this book summarizes recent developments in the non-asymptotic and asymptotic theory of variational Bayesian learning and suggests how this theory can be applied in practice. The authors begin by developing a basic framework with a focus on conjugacy, which enables the reader to derive tractable algorithms. Next, it summarizes non-asymptotic theory, which, although limited in application to bilinear models, precisely describes the behavior of the variational Bayesian solution and reveals its sparsity inducing mechanism. Finally, the text summarizes asymptotic theory, which reveals phase transition phenomena depending on the prior setting, thus providing suggestions on how to set hyperparameters for particular purposes. Detailed derivations allow readers to follow along without prior knowledge of the mathematical techniques specific to

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High-Dimensional Data Analysis with Low-Dimensional Models：Principles, Computation, and Applications

90折

17.

High-Dimensional Data Analysis with Low-Dimensional Models：Principles, Computation, and Applications

作者：John Wright 出版社：Cambridge Univ Pr 出版日：2021/12/31 裝訂：精裝

Connecting theory with practice, this systematic and rigorous introduction covers the fundamental principles, algorithms and applications of key mathematical models for high-dimensional data analysis. Comprehensive in its approach, it provides unified coverage of many different low-dimensional models and analytical techniques, including sparse and low-rank models, and both convex and non-convex formulations. Readers will learn how to develop efficient and scalable algorithms for solving real-world problems, supported by numerous examples and exercises throughout, and how to use the computational tools learnt in several application contexts. Applications presented include scientific imaging, communication, face recognition, 3D vision, and deep networks for classification. With code available online, this is an ideal textbook for senior and graduate students in computer science, data science, and electrical engineering, as well as for those taking courses on sparsity, low-dimensional str

定價：3779 元，優惠價：9 3401

無庫存，下單後進貨(到貨天數約45-60天)

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Statistical Learning With Sparsity ─ The Lasso and Generalizations

Sparsity—Graphs, Structures, and Algorithms

Algorithms for Sparsity-Constrained Optimization

Sparsity Methods for Systems and Control

Advanced Sparsity-Driven Models and Methods for Radar Applications

Study on Signal Detection and Recovery Methods with Joint Sparsity

Statistical Learning with Sparsity: The Lasso and Generalizations

Sparse Sensing and Sparsity Sensed in Multi-Sensor Array Applications

Estimation and Testing Under Sparsity ― Ecole D'ete De Probabilites De Saint-flour Xlv - 2015

Analyticity and Sparsity in Uncertainty Quantification for Pdes with Gaussian Random Field Inputs

電子商務推薦系統瓶頸問題研究（簡體書）

Computational Methods for Large Sparse Power Systems Analysis ― An Object Oriented Approach

Perspectives in Shape Analysis

Methods of Applied Mathematics for Engineers and Scientists ― Analytical and Numerical Approaches

An Introduction to Sparse Stochastic Processes

Variational Bayesian Learning Theory

High-Dimensional Data Analysis with Low-Dimensional Models：Principles, Computation, and Applications