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Maverick Sanchez
Maverick Sanchez

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One of the greatest challenges we have in addressing this subject is that our cultural norms of social interaction, engagement, and marriage are quite different in a number of ways to that of biblical history. Subsequently, many of the questions asked today are not directly addressed in Scripture. For example, it is evident in Scripture that the family structure conveyed a measure of protection and provision far exceeding that of our cultural norm. This is perhaps most apparent when we consider the fact that most of Scripture was written in cultural contexts where marriages were typically arranged by the parents. In these settings, the children often had little or no choice in the matter at all. Therefore, in the Early Church, a paper like this would have been quite irrelevant, unless very much changed in content and addressed to parents rather than singles. Suffice it to say, in biblical times the measure of governing authority over the lives of individuals was far weightier and more clearly defined than in our culture. Generally, Christians from the Western tradition would consider our freedom of choice a wonderful liberty, although some have argued for the benefits and merits of the historic model (usually those already married to the partner of their choice!). However, that discussion lies outside the scope of this paper. Our focus is finding and applying the wisdom of God within our current culture, rather than in making comparisons with another.

Since Sagittarians don't believe in being diplomatic, someone who wants to be their life partner should have the sensitivity to know that Sagittarians don't have any ill will in them. They are simply honest and free of any malice.

Before you read on, we thought you might like to download our 3 Positive Psychology Exercises for free. These science-based exercises will explore fundamental aspects of positive psychology including strengths, values and self-compassion and will give you the tools to enhance the wellbeing of your clients, students or employees.

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Problem setting Support vector machines (SVMs) are very popular tools for classification, regression and other problems. Due to the large choice of kernels they can be applied with, a large variety of data can be analysed using these tools. Machine learning thanks its popularity to the good performance of the resulting models. However, interpreting the models is far from obvious, especially when non-linear kernels are used. Hence, the methods are used as black boxes. As a consequence, the use of SVMs is less supported in areas where interpretability is important and where people are held responsible for the decisions made by models. Objective In this work, we investigate whether SVMs using linear, polynomial and RBF kernels can be explained such that interpretations for model-based decisions can be provided. We further indicate when SVMs can be explained and in which situations interpretation of SVMs is (hitherto) not possible. Here, explainability is defined as the ability to produce the final decision based on a sum of contributions which depend on one single or at most two input variables. Results Our experiments on simulated and real-life data show that explainability of an SVM depends on the chosen parameter values (degree of polynomial kernel, width of RBF kernel and regularization constant). When several combinations of parameter values yield the same cross-validation performance, combinations with a lower polynomial degree or a larger kernel width have a higher chance of being explainable. Conclusions This work summarizes SVM classifiers obtained with linear, polynomial and RBF kernels in a single plot. Linear and polynomial kernels up to the second degree are represented exactly. For other kernels an indication of the reliability of the approximation is presented. The complete methodology is available as an R package and two apps and a movie are provided to illustrate the possibilities offered by the method. PMID:27723811

Virtual screening is an important step in early-phase of drug discovery process. Since there are thousands of compounds, this step should be both fast and effective in order to distinguish drug-like and nondrug-like molecules. Statistical machine learning methods are widely used in drug discovery studies for classification purpose. Here, we aim to develop a new tool, which can classify molecules as drug-like and nondrug-like based on various machine learning methods, including discriminant, tree-based, kernel-based, ensemble and other algorithms. To construct this tool, first, performances of twenty-three different machine learning algorithms are compared by ten different measures, then, ten best performing algorithms have been selected based on principal component and hierarchical cluster analysis results. Besides classification, this application has also ability to create heat map and dendrogram for visual inspection of the molecules through hierarchical cluster analysis. Moreover, users can connect the PubChem database to download molecular information and to create two-dimensional structures of compounds. This application is freely available through PMID:25928885


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