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Multi-Label Dimensionality Reduction - Jieping (arizona State University Ye - Bog - Taylor & Francis Inc - Plusbog.dk

Multi-Label Dimensionality Reduction - Jieping (arizona State University Ye - Bog - Taylor & Francis Inc - Plusbog.dk

Similar to other data mining and machine learning tasks, multi-label learning suffers from dimensionality. An effective way to mitigate this problem is through dimensionality reduction, which extracts a small number of features by removing irrelevant, redundant, and noisy information. The data mining and machine learning literature currently lacks a unified treatment of multi-label dimensionality reduction that incorporates both algorithmic developments and applications. Addressing this shortfall, Multi-Label Dimensionality Reduction covers the methodological developments, theoretical properties, computational aspects, and applications of many multi-label dimensionality reduction algorithms. It explores numerous research questions, including: - - How to fully exploit label correlations for effective dimensionality reduction - How to scale dimensionality reduction algorithms to large-scale problems - How to effectively combine dimensionality reduction with classification - How to derive sparse dimensionality reduction algorithms to enhance model interpretability - How to perform multi-label dimensionality reduction effectively in practical applications - The authors emphasize their extensive work on dimensionality reduction for multi-label learning. Using a case study of Drosophila gene expression pattern image annotation, they demonstrate how to apply multi-label dimensionality reduction algorithms to solve real-world problems. A supplementary website provides a MATLAB ® package for implementing popular dimensionality reduction algorithms.

DKK 993.00
3

Multi-State Survival Models for Interval-Censored Data - Ardo Van Den Hout - Bog - Taylor & Francis Inc - Plusbog.dk

Multi-State Survival Models for Interval-Censored Data - Ardo Van Den Hout - Bog - Taylor & Francis Inc - Plusbog.dk

Multi-State Survival Models for Interval-Censored Data introduces methods to describe stochastic processes that consist of transitions between states over time. It is targeted at researchers in medical statistics, epidemiology, demography, and social statistics. One of the applications in the book is a three-state process for dementia and survival in the older population. This process is described by an illness-death model with a dementia-free state, a dementia state, and a dead state. Statistical modelling of a multi-state process can investigate potential associations between the risk of moving to the next state and variables such as age, gender, or education. A model can also be used to predict the multi-state process. The methods are for longitudinal data subject to interval censoring. Depending on the definition of a state, it is possible that the time of the transition into a state is not observed exactly. However, when longitudinal data are available the transition time may be known to lie in the time interval defined by two successive observations. Such an interval-censored observation scheme can be taken into account in the statistical inference. Multi-state modelling is an elegant combination of statistical inference and the theory of stochastic processes. Multi-State Survival Models for Interval-Censored Data shows that the statistical modelling is versatile and allows for a wide range of applications.

DKK 920.00
3

Practical Multi-Projector Display Design - Aditi Majumder - Bog - Taylor & Francis Inc - Plusbog.dk

Stepfamilies - A Multi-Dimensional Perspective - Roni (Adelphi University School of Social Work Berger

Programming for Hybrid Multi/Manycore MPP Systems - Aaron Vose - Bog - Taylor & Francis Inc - Plusbog.dk

Programming for Hybrid Multi/Manycore MPP Systems - Aaron Vose - Bog - Taylor & Francis Inc - Plusbog.dk

"Ask not what your compiler can do for you, ask what you can do for your compiler." --John Levesque, Director of Cray’s Supercomputing Centers of Excellence T he next decade of computationally intense computing lies with more powerful multi/manycore nodes where processors share a large memory space. These nodes will be the building block for systems that range from a single node workstation up to systems approaching the exaflop regime. The node itself will consist of 10’s to 100’s of MIMD (multiple instruction, multiple data) processing units with SIMD (single instruction, multiple data) parallel instructions. Since a standard, affordable memory architecture will not be able to supply the bandwidth required by these cores, new memory organizations will be introduced. These new node architectures will represent a significant challenge to application developers. Programming for Hybrid Multi/Manycore MPP Systems attempts to briefly describe the current state-of-the-art in programming these systems, and proposes an approach for developing a performance-portable application that can effectively utilize all of these systems from a single application. The book starts with a strategy for optimizing an application for multi/manycore architectures. It then looks at the three typical architectures, covering their advantages and disadvantages. The next section of the book explores the other important component of the target—the compiler. The compiler will ultimately convert the input language to executable code on the target, and the book explores how to make the compiler do what we want. The book then talks about gathering runtime statistics from running the application on the important problem sets previously discussed. How best to utilize available memory bandwidth and virtualization is covered next, along with hybridization of a program. The last part of the book includes several major applications, and examines future hardware advancements and how the application developer may prepare for those advancements.

DKK 819.00
3

Distributed Simulation - David A. Nash - Bog - Taylor & Francis Inc - Plusbog.dk

Leadership and Women in Statistics - Bog - Hardback

Smoking - David G. Gilbert - Bog - Taylor & Francis Inc - Plusbog.dk