Dataset Shift in Machine Learning

Dataset Shift in Machine Learning

Dataset shift is a common problem in predictive modeling that occurs when the joint distribution of inputs and outputs differs between training and test stages. Covariate shift, a particular case of dataset shift, occurs when only the input distribution changes. Dataset shift is present in most practical applications, for reasons ranging from the bias introduced by experimental design to the irreproducibility of the testing conditions at training time. (An example is -email spam filtering, which may fail to recognize spam that differs in form from the spam the automatic filter has been built on.) Despite this, and despite the attention given to the apparently similar problems of semi-supervised learning and active learning, dataset shift has received relatively little attention in the machine learning community until recently. This volume offers an overview of current efforts to deal with dataset and covariate shift.

Dataset Shift in Machine Learning

Manifesto of the New Economy

Manifesto of the New Economy

The book describes the main directions for the development of the digital society. The author angles its book to those who are interested to know what would replace search engines, and how social networks would evolve; what profit can be made of different forms of informational collaboration (crowdsourcing, collaborative filtering). And, the main thing, how it will influence the structure of the society and human pursuit for happiness. The author does not confine himself to a theory, he sets and solves practical questions: How talent, success and stardom are interconnected, how to make money in social networks, what is the business model for the development of entertainment and media, how to measure cultural values, and what is the subjective time of the individual and how to make it qualitative? There have been no answers to these questions before. Internet and social networks have provided tools and data that Alexander Dolgin was the first to use in economics.

Manifesto of the New Economy

Mathematical Principles of Signal Processing

Mathematical Principles of Signal Processing

Fourier analysis is one of the most useful tools in many applied sciences. The recent developments of wavelet analysis indicate that in spite of its long history and well-established applications, the field is still one of active research. This text bridges the gap between engineering and mathematics, providing a rigorously mathematical introduction of Fourier analysis, wavelet analysis and related mathematical methods, while emphasizing their uses in signal processing and other applications in communications engineering. The interplay between Fourier series and Fourier transforms is at the heart of signal processing, which is couched most naturally in terms of the Dirac delta function and Lebesgue integrals. The exposition is organized into four parts. The first is a discussion of one-dimensional Fourier theory, including the classical results on convergence and the Poisson sum formula. The second part is devoted to the mathematical foundations of signal processing sampling, filtering, digital signal processing. Fourier analysis in Hilbert spaces is the focus of the third part, and the last part provides an introduction to wavelet analysis, time-frequency issues, and multiresolution analysis. An appendix provides the necessary background on Lebesgue integrals.

Mathematical Principles of Signal Processing

Linux iptables Pocket Reference

Linux iptables Pocket Reference

Firewalls, Network Address Translation (NAT), , logging and accounting are all provided by Linux’s Netfilter system, also known by the name of the command used to administer it, iptables. The iptables interface is the most sophisticated ever offered onLinux and makes , an extremely flexible system for any kind of network filtering you might do. Large sets of filtering rules can be grouped in ways that makes it easy to test them and turn them on and off.,

Do you watch for all types of ICMP traffic – some of them quite dangerous? Can you take advantage of stateful filtering to simplify the management of TCP connections? Would you like to track how much traffic of various types you get?,

This pocket reference will help you at those critical moments when someone asks you to open or close a port in a hurry, either to enable some important traffic or to block an attack. The book will keep the subtle syntax straight and help you remember all the values you have to enter in order to be as secure as possible. The book has an introductory section that describes applications,followed by a reference / encyclopaedic section with all the matches and targets arranged alphabetically.

Linux iptables Pocket Reference

Linux Firewalls

Linux Firewalls

System administrators need to stay ahead of new , vulnerabilities that leave their networks exposed every day. A firewall and an intrusion detection systems (IDS) are two important weapons in that fight, enabling you to proactively deny , and monitor , traffic for signs of an attack.,

Linux Firewalls discusses the technical details of the iptables firewall and the Netfilter framework that are built into the Linux kernel, and it explains how they provide strong filtering, Network Address Translation (NAT), state tracking, and application layer inspection capabilities that rival many commercial tools. You’ll learn how to deploy iptables as an IDS with psad and fwsnort and how to build a strong, passive authentication layer around iptables with fwknop.

Linux Firewalls