Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Controlling chaos

In the Hamadryas baboon, males are substantially larger than females. A troop of baboons is subdivided into a number of ‘one-male groups’, consisting of one adult male and one or more females with their young. The male prevents any of ‘his’ females from moving too far from him. Kummer (1971) performed the following experiment. Two males, A and B, previously unknown to each other, were placed in a large enclosure. Male A was free to move about the enclosure, but male B was shut in a small cage, from which he could observe A but not interfere. A female, unknown to both males, was then placed in the enclosure. Within 20 minutes male A had persuaded the female to accept his ownership. Male B was then released into the open enclosure. Instead of challenging male A , B avoided any contact, accepting A’s ownership.

Evolution and the Theory of Games

In recent years, a large amount of work on chaos-based cryptosystems have been published. However, many of the proposed schemes fail to explain or do not possess a number of features that are fundamentally important to all kind of cryptosystems. As a result, many proposed systems are difficult to implement in practice with a reasonable degree of security. Likewise, they are seldom accompanied by a thorough security analysis. Consequently, it is difficult for other researchers and end users to evaluate their security and performance. This work is intended to provide a common framework of basic guidelines that, if followed, could benefit every new cryptosystem. The suggested guidelines address three main issues: implementation, key management and security analysis, aiming at assisting designers of new cryptosystems to present their work in a more systematic and rigorous way to fulfill some basic cryptographic requirements. Meanwhile, several recommendations are made regarding some practical aspects of analog chaos-based secure communications, such as channel noise, limited bandwith and attenuation.

/pdf/some-basic-cryptographic-requirements-for-chaos-based-4qsyrod8nl.pdf

Some basic cryptographic requirements for chaos-based cryptosystems

The possibility of synchronization of systems inherently operating in a chaotic mode is analyzed. The Pecora-Carroll concept of synchronizable response subsystems and chaotic driving is described. Possibilities of synchronization using linear coupling of the chaotic systems are also considered. Potential applications of synchronized chaotic systems in signal processing are discussed and analyzed in examples using Chua's circuits. >

Taming chaos. I. Synchronization

Linear versus nonlinear circuits steady-state behaviour chaos and complexity - definition problems basic mathematical models and tools laboratory measurements in chaotic circuits application specific equipment computer simulations in chaotic circuits characterization of complex phenomena from experimental data quantifying complex phenomena interpretation of results examples - RC-generator, second-order digital filter, second-order circuit with sinusoidal driving, other examples spectral properties of chaotic circuits noise generation controlling chaotic circuits and electronic chaos controllers chaotic synchronization and applications in broad-band communication and data security.

Chaos and Complexity in Nonlinear Electronic Circuits

Keywords: chaos ; Non-Linear Signal Processing Reference LANOS-CONF-1997-015 Record created on 2004-12-03, modified on 2017-05-12

Identification of chaotic systems based on adaptive synchronization

Global relative parameter sensitivities of the feed-forward loops in genetic networks

For pt. I see ibid., vol. 40, no. 10, p. 693-9 (Oct. 1993). Various control concepts developed for chaotic systems are described in this paper. By control, we mean influencing the system operating in a chaotic regime in such a way as to achieve a desired type of dynamic behavior ("goal") that typically could be a fixed point or periodic orbit. The control concepts described here include system parameter variation, chaotic oscillation absorber, entrainment, linear feedback control, and methods for stabilizing unstable periodic orbits. Advantages and drawbacks of these methods are described. >

Maciej Ogorzalek

Papers

Taming chaos. I. Synchronization

Chaos and Complexity in Nonlinear Electronic Circuits

Identification of chaotic systems based on adaptive synchronization

Global relative parameter sensitivities of the feed-forward loops in genetic networks

Taming chaos. II. Control