The analysis of experimental data is at heart of science from its beginnings. But it was the advent of digital computers that allowed the execution of highly non-linear and increasingly complex data analysis procedures - methods that were completely unfeasible before. Non-linear curve fitting, clustering and machine learning belong to these modern techniques which are a further step towards computational intelligence. The goal of this book is to provide an interactive and illustrative guide to these topics. It concentrates on the road from two dimensional curve fitting to multidimensional clu
We examine the learning-curve sampling method, an approach for applying machinelearning algorithms t...
During the past decade there has been an explosion in computation and information tech-nology. With ...
Data analysis is changing fast. Driven by a vast range of application domains and affordable tools, ...
This successful book provides in its second edition an interactive and illustrative guide from two-d...
Curve fitting is one of the procedures in data analysis and is helpful for prediction analysis showi...
Extrapolation of the learning curve provides an estimation of how much data is needed to achieve the...
First volume in a three-part series. Book written by Sandra Lach Arlinghaus. Material underwent ex...
This article describes a Bayesian-based method for solving curve fitting problems. We extend the bas...
Part 9: Pattern RecognitionInternational audienceThis article describes a Bayesian-based method for ...
The problem of curve matching and clustering (or classifi-cation) represents an important problem ap...
In this article, we present a new method based on extreme learning machine (ELM) algorithm for solvi...
To classify objects based on their features and characteristics is one of the most important and pri...
Curve-fitting problems are widely solved using numerical and soft techniques. In particular, artific...
Machine learning techniques have the potential of alleviating the complexity of knowledge acquisitio...
Abstract. We are increasingly confronted with ”Big Data”, but the challenge is not the size of this ...
We examine the learning-curve sampling method, an approach for applying machinelearning algorithms t...
During the past decade there has been an explosion in computation and information tech-nology. With ...
Data analysis is changing fast. Driven by a vast range of application domains and affordable tools, ...
This successful book provides in its second edition an interactive and illustrative guide from two-d...
Curve fitting is one of the procedures in data analysis and is helpful for prediction analysis showi...
Extrapolation of the learning curve provides an estimation of how much data is needed to achieve the...
First volume in a three-part series. Book written by Sandra Lach Arlinghaus. Material underwent ex...
This article describes a Bayesian-based method for solving curve fitting problems. We extend the bas...
Part 9: Pattern RecognitionInternational audienceThis article describes a Bayesian-based method for ...
The problem of curve matching and clustering (or classifi-cation) represents an important problem ap...
In this article, we present a new method based on extreme learning machine (ELM) algorithm for solvi...
To classify objects based on their features and characteristics is one of the most important and pri...
Curve-fitting problems are widely solved using numerical and soft techniques. In particular, artific...
Machine learning techniques have the potential of alleviating the complexity of knowledge acquisitio...
Abstract. We are increasingly confronted with ”Big Data”, but the challenge is not the size of this ...
We examine the learning-curve sampling method, an approach for applying machinelearning algorithms t...
During the past decade there has been an explosion in computation and information tech-nology. With ...
Data analysis is changing fast. Driven by a vast range of application domains and affordable tools, ...