Kategoria BISAC: Computers >> Machine Theory

Pattern theory is a distinctive approach to the analysis of all forms of real-world signals. At its core is the design of a large variety of probabilistic models whose samples reproduce the look and feel of the real signals, their patterns, and their variability. Bayesian statistical inference then allows you to apply these models in the analysis of new signals.

This book treats the mathematical tools, the models themselves, and the computational algorithms for applying statistics to analyze six representative classes of signals of increasing complexity. The book covers patterns in...

In machine learning applications, practitioners must take into account the cost associated with the algorithm. These costs include:

• Cost of acquiring training data
• Cost of data annotation/labeling and cleaning
• Computational cost for model fitting, validation, and testing
• Cost of collecting features/attributes for test data
• Cost of user feedback collection
• Cost of incorrect prediction/classification

Cost-Sensitive Machine Learning is one of the first books to provide an overview of...

Due to advances in sensor, storage, and networking technologies, data is being generated on a daily basis at an ever-increasing pace in a wide range of applications, including cloud computing, mobile Internet, and medical imaging. This large multidimensional data requires more efficient dimensionality reduction schemes than the traditional techniques. Addressing this need, multilinear subspace learning (MSL) reduces the dimensionality of big data directly from its natural multidimensional representation, a tensor.

Multilinear Subspace Learning: Dimensionality Reduction of...

Used by companies, organizations, and even individuals to promote recognition of their brand, logos can also act as a valuable means of identifying the source of a document. E-business applications can retrieve and catalog products according to their logos. Governmental agencies can easily inspect goods using smart mobile devices that use logo recognition techniques. However, because logos are two-dimensional shapes of varying complexity, the recognition process can be challenging. Although promising results have been found for clean logos, they have not been as robust for noisy logos....

Data analysis is changing fast. Driven by a vast range of application domains and affordable tools, machine learning has become mainstream. Unsupervised data analysis, including cluster analysis, factor analysis, and low dimensionality mapping methods continually being updated, have reached new heights of achievement in the incredibly rich data world that we inhabit.

Statistical Learning and Data Science is a work of reference in the rapidly evolving context of converging methodologies. It gathers contributions from some of the foundational thinkers in the different...

A significant amount of effort in neural modeling is directed towards understanding the representation of information in various parts of the brain, such as cortical maps 6], and the paths along which sensory information is processed. Though the time domain is integral an integral aspect of the functioning of biological systems, it has proven very challenging to incorporate the time domain effectively in neural network models. A promising path that is being explored is to study the importance of synchronization in biological systems. Synchronization plays a critical role in the...

This book introduces readers to the basic concepts, classical approaches, and the newest design, development, and applications of biometrics. It also provides a glimpse of future designs and research directions in biometrics. In addition, it discusses some latest concerns and issues in this area. Suitable for a wide range of readers, the book explains professional terms in plain English. Some concepts and designs discussed are so new that commercial systems based on them may not arrive in the market in the next 10 to 20 years.

Demand continues to grow worldwide, from both government and commerce, for technologies capable of automatically selecting and identifying object/human behaviour.

This accessible text/reference presents a comprehensive and unified treatment of visual analysis of behaviour from computational-modelling and algorithm-design perspectives. The book provides in-depth discussion on computer vision and statistical machine learning techniques, in addition to reviewing a broad range of behaviour modelling problems. A mathematical background is not required to understand the content, although...