NeuroShell Classifier从设计之初就专一于解决分类和决策问题。它能基于从汗青案例中进建到的类别，鉴别新数据中的类别。其输出了局即为类别，例如：{癌症，良性}、{买入，卖出，持有}、{酸性，中性，碱性}、{高度合格，合格，不合格}、{优胜方，裁减方}、{产品1，产品2，…，产品N}、{决策1，决策2，…，决策N}。与 NeuroShell Predictor一致，它选取了新版的专有neuro和遗传分类算法，无需设置其他参数。这些算法使neural网络支持读写文本文件。

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分类算法是经给多年钻研的优质成就。从前那种必要精心设置数十个参数能力构建优良模型而又预防过拟合的日子一去不复返了。那种必要礼聘neural网络专家或统计学家来构建预测模型的日子也一去不复返了。

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除了使用过于难题之表，以往分类系统常见的两大痛点是：它们运行速度太慢，或者无法正确通知你每个变量对模型的沉要性有多高。我们已经解决了这些问题。这就是为什么我们提供了两种可供选择的训练模型：

1. “neural步骤”：基于一种名为 Turboprop2 的算法。Turboprop2 是卡内基梅隆大学 Scott Fahlman 发现的级联有关算法的一个变种。Turboprop2 能动态增长暗藏neure，并且训练速度出格快。与旧式neural网络必要数幼时训练相比，Turboprop2 模型只需几秒钟即可构建实现。

2. “遗传训练步骤”：是基于 Donald Specht 发现的概率neural网络的一种遗传算法变体。它在样本表模式下进行训练
   ；性质上是在执行一种“留一法”技术，也称为“刀切法”或“交叉验证”。若是您使用此步骤进行训练，您现实上是在以样本表的方式对待训练集。因而，当您没有足够的训练样本时，此步骤极其有效。随着训练集中样本数量的增长，遗传训练步骤所需的功夫会更长。

遗传步骤会提供对自变量输入的分析，援手您确定哪些变量在您的模型中尤为沉要。

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?NeuroShell Classifier使用起来尤为单一，无需使用手册！取而代之的是一个“领导者”，它会疏导您实现分类模型的构建过程。在领导者的每个阶段，我们详尽的援手文件城市为您提供所需的所有信息。当您通过领导者进建后，能够将其关关，直接使用工具栏或菜单进行执行。法式蕴含一个在线的、高低文有关的参考手册，您能够自行打印或直接在电脑上浏览。

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对于那些但愿将天生的neural网络模型嵌入到您自己的法式中，或者分发了局的用户，我们提供了一个可选的运行时服务器(Run-Time Server)。模型能够分发，无需支付版权费或其他用度。

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? NeuroShell Classifier能够读取从电子表格导出的数据，并将其显示在数据网格中

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? 能够选择陆续或随机的数据行作为训练集和样本表集

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? 能够从数据文件的列当选择输入变量和进展的输出。您还能够选择neural训练步骤或遗传训练步骤

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? 与必要大量“参数微调”的旧式反向传布算法分歧，neural步骤只必要设置一个参数

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? 遗传训练步骤提供了三种现代优化技术以及优化指标的选择。自界说适应度矩阵允许您对某些分类谬误和成功进行惩
；蚯康，例如，对假阴性的惩治大于假阳性

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? 训练实现后，neural网络能够利用于训练数据或样本表数据，并显示合用于分类的各类统计信息

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? 分类是基于概率进行的。若是您愿意，能够以分歧方式诠释这些概率

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? 受试者工作特点曲线是一种盛行的图形化步骤，用于总结分类模型的总体效能

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【英文介绍】

The NeuroShell Classifier was crafted from the beginning to excel at solving classification and decision making problems. NeuroShell Classifier can detect categories in new data based upon the categories it learned from case histories.? Outputs are categories such as {cancer, benign}, {buy, sell, hold}, {acidic, neutral, alkaline}, {highly qualified, qualified, unqualified}, {winner, loser}, {product 1, product 2, … , product N}, {decision 1, decision2, … , decision N}. Like the NeuroShell?Predictor, it has the latest proprietary neural and genetic classifiers with no parameters to set. These are our most powerful neural networks. It reads and writes text files.???

The classification algorithms are the crowning achievement of several years of research. Gone are the days of dozens of parameters that must be artistically set to create a good model without over-fitting. Gone are the days of hiring a neural net expert or a statistician to build your predictive models.

Two of the most commonly heard complaints about previous classification systems, aside from being too hard to use, are that they are too slow or that they do not accurately tell you how important each of the variables is to the model. We've taken care of those problems. That's why we have two training models from which to choose:

1. The first training method, which we call the “neural method” is based on an algorithm called Turboprop2, a variant of the famous Cascade Correlation algorithm invented at Carnegie Mellon University by Scott Fahlman. TurboProp2 dynamically grows hidden neurons and trains very fast.??TurboProp2 models are built (trained) in a matter of seconds compared to hours for older neural networks types.???

2. The second method, the “genetic training method”, is a genetic algorithm variation of the Probabilistic neural Net (PNN) invented by Donald Specht. It trains everything in an out-of-sample mode; it is essentially doing a "one-hold-out" technique, also called "jackknife" or "cross validation".? If you train using this method, you are essentially looking at the training set out-of-sample.? This method is therefore extremely effective when you do not have many patterns on which to train.?The genetic training method takes longer to train as more patterns are added to the training set.

The genetic method provides an analysis of independent variables (inputs) to help you determine which?ones are most important in your model.

The NeuroShell Classifier is so easy to use that it doesn't need a manual! Instead, there is an "Instructor" that guides you through making the classification models. At every stage of the Instructor, our extensive help file will give you all the information you need. When you have learned from the Instructor, you can turn it off and work from the toolbar or menus.? The program does includes an on-line, context sensitive reference manual that you may print yourself or just browse from your computer.

Finally, for those who want to embed the resulting neural models into your own programs, or to distribute the results, there is an optional?Run-Time Server?available. Classifier models may be distributed without incurring royalties or other fees.

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