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

The current research investigated the effectiveness of solutionfocused brief therapy in the improvement of communication patterns and marital intimacy among married women. This quasiexperimental research used pretest-posttest design with control group. The statistical population consisted of married women referred to the one of the health houses of Tehran city. 24 women was selected according to available sampling method. Based on the obtained score on communication patterns scale and marital intimacy scale the Participants were assigned randomly in two experimental and control groups (12 Participants per group). The experimental group participated in 7 sessions of group solutionfocused therapy and the control group received no treatment. The results show that there were significant differences between the pretest and posttest scores of the experimental group, indicating that the solution-focused brief therapy could significantly improve communication patterns and increase marital intimacy among married women. Thus, interventions based on solution-focused brief therapy could be useful in improving the communication patterns and increasing the marital intimacy.

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Effectiveness of solution-focused brief couple therapy in improvement of communication patterns and marital intimacy in women

We consider the distance from a (square or rectangular) matrix pencil to the nearest matrix pencil in 2-norm that has a set of specified eigenvalues. We derive a singular value optimization characterization for this problem and illustrate its usefulness for two applications. First, the characterization yields a singular value formula for determining the nearest pencil whose eigenvalues lie in a specified region in the complex plane. For instance, this enables the numerical computation of the nearest stable descriptor system in control theory. Second, the characterization partially solves the problem posed in [Boutry et al. 2005] regarding the distance from a general rectangular pencil to the nearest pencil with a complete set of eigenvalues. The involved singular value optimization problems are solved by means of BFGS and Lipschitz-based global optimization algorithms.

Generalized Eigenvalue Problems with Specified Eigenvalues

We consider the distance from a (square or rectangular) matrix pencil to the nearest matrix pencil in 2-norm that has a set of specied eigenvalues. We derive a singular value optimization characterization for this problem and illustrate its usefulness for two applications. First, the characterization yields a singular value formula for determining the nearest pencil whose eigenvalues lie in a specied region in the complex plane. For instance, this enables the numerical computation of the nearest stable descriptor system in control theory. Second, the characterization partially solves the problem posed in [Boutry et al. 2005] regarding the distance from a general rectangular pencil to the nearest pencil with a complete set of eigenvalues. The involved singular value optimization problems are solved by means of BFGS and Lipschitz-based global optimization algorithms.

/pdf/generalized-eigenvalue-problems-with-specified-eigenvalues-2z6mhj7xqq.pdf

Generalized eigenvalue problems with specified eigenvalues

Background and aim: Quality of work life is one of the most important factors that empowers human resources in healthcare system. It also improves employee’s job satisfaction. Because nursing is an important component of health care systems of a country, this survey carried out to determine the work-related quality of life of nurses in Ardabil province hospitals. Methods: This was a descriptive cross-sectional study which was performed by census sampling method on all nursing staff working in hospitals in Ardabil province. 812 nurses returned questionnaires. Data collection was done by a two-part questionnaire, consisted of demographic and quality of work life questionnaire. Data were analyzed using SPSS v15 and descriptive and analytical statistical methods. Results: In this study, quality of work life of nurses was moderate (83.7%) and only 4.9% of them had a good quality of work life. There was a statistically significant correlation between quality of work life and employment status, shift work, hospital work and satisfaction with field of study

Study of Work-Related Quality of Life of Nurses in Ardabil Province Hospitals

The 2-norm distance from a matrix A to the set ${\mathcal{M}}$ of n × n matrices with a zero eigenvalue of multiplicity ≥3 is estimated. If 

$$Q(\gamma _1 ,\gamma _2 ,\gamma _3 ) = \left( {\begin{array}{*{20}c} A & {\gamma _1 I_n } & {\gamma _3 I_n } \\ 0 & A & {\gamma _2 I_n } \\ 0 & 0 & A \\ \end{array} } \right), n \geqslant 3,$$

then 

$$\rho _2 (A,{\mathcal{M}}) \geqslant {\mathop {max}\limits_{\gamma _1 ,\gamma _2 \geqslant 0,\gamma _3 \in {\mathbb{C}}}} \sigma _{3n - 2} (Q(\gamma _1 ,\gamma _2 ,\gamma _3 )),$$

where σi(·)is the ith singular value of the corresponding matrix in the decreasing order of singular values. Moreover, if the maximum on the right-hand side is attained at the point $\gamma ^ * = (\gamma _1^ * ,\gamma _2^ * ,\gamma _3^ * )$, where $\gamma _1^ * \gamma _2^ * \ne 0$, then, in fact, one has the exact equality 

$$\rho _2 (A,{\mathcal{M}}) = \sigma _{3n - 2} (Q(\gamma _1^ * ,\gamma _2^ * ,\gamma _3^ * )).$$

This result can be regarded as an extension of Malyshev's formula, which gives the 2-norm distance from A to the set of matrices with a multiple zero eigenvalue.

A. M. Nazari

Papers

On the Distance to the Closest Matrix with Triple Zero Eigenvalue

A study of relationship between qulaity of work life and work-family conflict with job performance among married men employee

The inverse eigenvalue problem for symmetric quasi anti-bidiagonal matrices

Computational aspect to the nearest matrix with two prescribed eigenvalues

On the inverse eigenvalue problem for nonnegative matrices of order two to five