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

Data Mining - Concepts and Techniques.

1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

The organization of behavior: A neuropsychological theory

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

The Paper presents a constitutive model for describing the stress-strain behaviour of partially saturated soils. The model is formulated within the framework of hardening plasticity using two iodependent sets of stress variables: the excess of total stress over air pressure and the suction. The mode1 is able to represent, in a consistent and unified manner, many of the fundamental features of the behaviour of partially saturated soils which had been treated separately by previously proposed models. On reaching saturation, the mode1 becomes a conventional critical state model. Because experimental evidence is still limited, the model has been kept as simple as possible in order to provide a basic framework from which extensions are possible. Tbe mode1 is intended for partially saturated soils which are slightly or moderately expansive. After formulating the model for isotropic and biaxial stress states, typical predictions are described and compared, in a qualitative way, with characteristic trends of the behaviour of partially saturated soils. Afterwards, the results of a number of suction-controlled laboratory tests on compacted kaolin and a sandy clay are used to evaluate the ability of the model to reproduce, quantitatively, observed behaviour. The agreement between observed and computed results is considered satisfactory and confirms the possibilities of reproducing the most important features of partially saturated soil behaviour using a simple general framework.

A constitutive model for partially saturated soils

The Paper presents a constitutive model for describing the stress-strain behaviour of partially saturated soils. The model is formulated within the framework of hardening plasticity using two independent sets of stress variables: the excess of total stress over air pressure and the suction. The model is able to represent, in a consistent and unified manner, many of the fundamental features of the behaviour of partially saturated soils which had been treated separately by previously proposed models. On reaching saturation, the model becomes a conventional critical state model. Because experimental evidence is still limited, the model has been kept as simple as possible in order to provide a basic framework from which extensions are possible. The model is intended for partially saturated soils which are slightly or moderately expansive. After formulating the model for isotropic and triaxial stress states, typical predictions are described and compared, in a qualitative way, with characteristic trends of the...

/pdf/a-constitutive-model-for-partially-saturated-soils-2qcca1f1md.pdf

A Constitutive Model for Partially Saturated Soils

The paper presents a framework for describing the mechanical behaviour of unsaturated expansive clays. It is an extension of an existing formulation developed for unsaturated soils of low activity....

A framework for the behaviour of unsaturated expansive clays

Presents numerical aspects of the program CODE_BRIGHT, which is a simulator for COupled DEformation, BRIne, Gas and Heat transport problems. It solves the equations of mass and energy balance and stress equilibrium and, originally, it was developed for saline media. The governing equations also include a set of constitutive laws and equilibrium conditions. The main peculiarities of saline media are in the dissolution/precipitation phenomena, presence of brine inclusions in the solid salt and creep deformation of the solid matrix.

/pdf/numerical-formulation-for-a-simulator-code-bright-for-the-1z1eiwxzd5.pdf

Numerical formulation for a simulator (CODE_BRIGHT) for the coupled analysis of saline media

https://upcommons.upc.edu/bitstream/handle/2117/2214/20.pdf;sequence=1

Eduardo Alonso

Papers

A constitutive model for partially saturated soils

A Constitutive Model for Partially Saturated Soils

A framework for the behaviour of unsaturated expansive clays

Numerical formulation for a simulator (CODE_BRIGHT) for the coupled analysis of saline media

Modelling the mechanical behaviour of expansive clays