Overview simple tasks using

Armed with reasonably good knowledge or hypotheses of the underlying biochemistry and the ability to formulate models based on physico-chemical principles, the models must be implemented to obtain and analyze the quantitative results and to generate predictions. Except in very simple, idealized cases, this task requires the use of various numerical methods and tools. In this section, an overview of the various model types and associated methods is offered. 

We begin by outlining the task and suggesting why a reaction path approach to evaluating a PES is plausible. Three important technical questions must then be addressed Exactly how do we define a reaction path, how do we calculate it, and how do we use it to describe a PES Because this task lies along the interface between ab initio quantum chemistry and reaction dynamics, even these three technical questions cannot be answered completely without reference to how we intend to evaluate the reaction dynamics. So when we go on to describe how the reaction dynamics is to be studied, we have to return from time to time to the questions of what path and what PES. Without claiming to cover the area exhaustively, we hope to arrive at a reasonable overview of how the simple reaction path approach works in practice. This is an active area of research, and some very useful reviews have already appeared [10-12]. 

Financial ratios are condensed data reporting quantifiable facts. With their help, complicated facts, structures, and procedures of corporations are depicted in a simple way to permit a fast and comprehensive overview. Thus, financial ratios are appropriate for the complex task of comparative credit quality assessment. To simplify the methodology, the number of financial ratios used should not be too large, and every financial ratio must be economically plausible. The selection of particularly appropriate financial ratios is a significant component of every rating methodology. These financial ratios should represent areas of relevance for creditors as exactly as possible, like debt/equity ratio, profitability, and liquidity. In this context, not only the level of these financial ratios is important but also their development over time. 

Finding an SVM model with good prediction statistics is a trial-and-error task. The objective is to maximize the predictions statistics while keeping the model simple in terms of number of input descriptors, number of support vectors, patterns used for training, and kernel complexity. In this section, we present an overview of the techniques used in SVM model optimization. 

---