NeuroShell Classifier - 神经网络分类软件

NeuroShell Classifier从设计之初就专注于解决分类和决策问题。它能基于从历史案例中学习到的类别，识别新数据中的类别。其输出结果即为类别，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，淘汰方}、{产品1，产品2，…，产品N}、{决策1，决策2，…，决策N}。与 NeuroShell Predictor一致，它采用了新版的专有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。

分类算法是经给多年研究的优质成果。过去那种需要精心设置数十个参数才能构建良好模型而又避免过拟合的日子一去不复返了。那种需要聘请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。

除了使用过于困难之外，以往分类系统常见的两大痛点是：它们运行速度太慢，或者无法准确告诉你每个变量对模型的重要性有多高。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方法”：基于一种名为 Turboprop2 的算法。Turboprop2 是卡内基梅隆大学 Scott Fahlman 发明的级联相关算法的一个变种。Turboprop2 能动态增加隐藏neure，并且训练速度特别快。与旧式neural网络需要数小时训练相比，Turboprop2 模型只需几秒钟即可构建完成。

2. “遗传训练方法”：是基于 Donald Specht 发明的概率neural网络的一种遗传算法变体。它在样本外模式下进行训练；本质上是在执行一种“留一法”技术，也称为“刀切法”或“交叉验证”。如果您使用此方法进行训练，您实际上是在以样本外的方式看待训练集。因此，当您没有足够的训练样本时，此方法极其有效。随着训练集中样本数量的增加，遗传训练方法所需的时间会更长。

遗传方法会提供对自变量输入的分析，帮助您确定哪些变量在您的模型中尤为重要。

 NeuroShell Classifier使用起来尤为简单，无需使用手册！取而代之的是一个“指导者”，它会引导您完成分类模型的构建过程。在指导者的每个阶段，我们详尽的帮助文件都会为您提供所需的所有信息。当您通过指导者学习后，可以将其关闭，直接使用工具栏或菜单进行执行。程序包含一个在线的、上下文相关的参考手册，您可以自行打印或直接在电脑上浏览。

对于那些希望将生成的neural网络模型嵌入到您自己的程序中，或者分发结果的用户，我们提供了一个可选的运行时服务器(Run-Time Server)。模型可以分发，无需支付版权费或其他费用。

• NeuroShell Classifier可以读取从电子表格导出的数据，并将其显示在数据网格中

• 可以选择连续或随机的数据行作为训练集和样本外集

• 可以从数据文件的列中选择输入变量和期望的输出。您还可以选择neural训练方法或遗传训练方法

• 与需要大量“参数微调”的旧式反向传播算法不同，neural方法只需要设置一个参数

• 遗传训练方法提供了三种现代优化技术以及优化目标的选择。自定义适应度矩阵允许您对某些分类错误和成功进行惩罚或强调，例如，对假阴性的惩罚大于假阳性

• 训练完成后，neural网络可以应用于训练数据或样本外数据，并显示适用于分类的各种统计信息

• 分类是基于概率进行的。如果您愿意，可以以不同方式解释这些概率

• 受试者工作特征曲线是一种流行的图形化方法，用于总结分类模型的总体效率

【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.