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1. 发明授权

US08682813B2 Sample class prediction method, prediction program, and prediction apparatus 有权
标题翻译：样本类预测方法，预测程序和预测装置
公开(公告)号：US08682813B2
公开(公告)日：2014-03-25
申请号：US13019683
申请日：2011-02-02
申请人： Kohtarou Yuta
发明人： Kohtarou Yuta
IPC分类号： G06K9/62 , G06F19/00
CPC分类号： G06K9/6286 , G06F19/24 , G06F19/704 , G06F19/707 , G06K9/6234 , G06K9/6256
摘要： To predict the class of an unknown sample, a) a discriminant function for assigning each training sample to class 1 or class 2 is obtained, b) the discriminant score of each training sample and an unknown sample are calculated using the function, c) it is determined whether the score of the unknown sample is either not smaller than the largest score or not larger than the smallest score taken among all of the training samples, d) if the determination in c) is affirmative, the class of the unknown sample is determined based on the score of the unknown sample, e) if the determination in c) is negative, then the training samples having the largest score and the smallest score are removed to form a new training sample set from remaining training samples, and f) a) to e) are repeated.
摘要翻译：为了预测未知样本的类别，a）获得将每个训练样本分配给1类或2类的判别函数，b）使用该函数计算每个训练样本和未知样本的判别分数，c）确定未知样本的得分是否不小于最大得分或不大于所有训练样本中取得的最小得分，d）如果c）中的确定是肯定的，则未知样本的类别是根据未知样本的得分确定，e）如果c）中的确定为否定，则删除具有最大得分和最小得分的训练样本，以形成剩余训练样本的新训练样本集，f） a）至e）重复。

2. 发明申请

US20100070441A1 Method, apparatus, and program for generating prediction model based on multiple regression analysis 有权
标题翻译：基于多元回归分析生成预测模型的方法，装置和程序
公开(公告)号：US20100070441A1
公开(公告)日：2010-03-18
申请号：US12585512
申请日：2009-09-16
申请人： Kohtarou Yuta
发明人： Kohtarou Yuta
IPC分类号： G06F15/18 , G06F17/18 , G06N5/02
CPC分类号： G06K9/6267 , G06N99/005
摘要： An objective variable prediction model based on multiple regression analysis and having high prediction accuracy is generated by a computer. The method includes the steps of: a) constructing an initial sample set from samples whose measured value of an objective variable is known; b) obtaining a calculated value of the objective variable using multiple regression analysis; c) extracting samples whose difference between the measured and the calculated value is not larger than a first value, and calculating a determination coefficient by applying multiple regression analysis to the extracted samples; d) repeating the step c) by changing the first value until the determination coefficient exceeds a second value; and e) performing two-class classification to classify the sub-sample set obtained at the end of the step d) as a first sub-sample set and remaining samples as a second sub-sample set, and calculating a discriminant function.
摘要翻译：通过计算机生成基于多元回归分析并具有高预测精度的客观变量预测模型。该方法包括以下步骤：a）从已知目标变量的测量值的样本构建初始样本集; b）使用多元回归分析获得目标变量的计算值; c）提取测量值和计算值之间的差不大于第一值的样本，并通过对提取的样本应用多元回归分析来计算确定系数; d）通过改变第一值直到确定系数超过第二值来重复步骤c）; 以及e）执行两类分类以将在步骤d）结束时获得的子样本集合分类为第一子样本集合，并且将剩余样本作为第二子样本集进行分类，并计算判别函数。

3. 发明授权

US08255342B2 Method, apparatus, and program for generating prediction model based on multiple regression analysis 有权
标题翻译：基于多元回归分析生成预测模型的方法，装置和程序
公开(公告)号：US08255342B2
公开(公告)日：2012-08-28
申请号：US12585512
申请日：2009-09-16
申请人： Kohtarou Yuta
发明人： Kohtarou Yuta
IPC分类号： G06F15/18
CPC分类号： G06K9/6267 , G06N99/005
摘要： An objective variable prediction model based on multiple regression analysis and having high prediction accuracy is generated by a computer. The method includes the steps of: a) constructing an initial sample set from samples whose measured value of an objective variable is known; b) obtaining a calculated value of the objective variable using multiple regression analysis; c) extracting samples whose difference between the measured and the calculated value is not larger than a first value, and calculating a determination coefficient by applying multiple regression analysis to the extracted samples; d) repeating the step c) by changing the first value until the determination coefficient exceeds a second value; and e) performing two-class classification to classify the sub-sample set obtained at the end of the step d) as a first sub-sample set and remaining samples as a second sub-sample set, and calculating a discriminant function.
摘要翻译：通过计算机生成基于多元回归分析并具有高预测精度的客观变量预测模型。该方法包括以下步骤：a）从已知目标变量的测量值的样本构建初始样本集; b）使用多元回归分析获得目标变量的计算值; c）提取测量值和计算值之间的差不大于第一值的样本，并通过对提取的样本应用多元回归分析来计算确定系数; d）通过改变第一值直到确定系数超过第二值来重复步骤c）; 以及e）执行两类分类以将在步骤d）结束时获得的子样本集合分类为第一子样本集合，并且将剩余样本作为第二子样本集进行分类，并计算判别函数。

4. 发明授权

US07725413B2 Generating two-class classification model for predicting chemical toxicity 有权
标题翻译：生成用于预测化学毒性的两类分类模型
公开(公告)号：US07725413B2
公开(公告)日：2010-05-25
申请号：US12453247
申请日：2009-05-04
申请人： Kohtarou Yuta
发明人： Kohtarou Yuta
IPC分类号： G06F15/18 , G06E1/00
CPC分类号： G06F17/30412 , G06F17/30707 , G06F19/707 , G06N99/005
摘要： A method includes: a) preparing as training data a sample set that contains a plurality of samples belonging to a first class and a plurality of samples belonging to a second class; b) generating, by performing discriminant analysis on the sample set, a first discriminant function having a high classification characteristic for the first class and a second discriminant function having a high classification characteristic for the second class; c) by classifying the sample set using the first and second discriminant functions, isolating any sample whose classification results by the first and second discriminant functions do not match; d) forming a new sample set by grouping together any sample thus isolated, and repeating b) and c) by using the new sample set; and e) causing d) to stop when the number of samples each of whose classification results do not match in c) has decreased to or below a predetermined value.
摘要翻译：一种方法包括：a）准备包含属于第一类的多个样本和属于第二类的多个样本的样本集合作为训练数据; b）通过对样本集进行判别分析，生成第一类具有高分类特性的第一判别函数和对第二类具有高分类特性的第二判别函数; c）通过使用第一和第二判别函数对样本集进行分类，分离由第一和第二判别函数分类结果不匹配的任何样本; d）通过将所分离的样品分组在一起形成新的样品组，并通过使用新的样品组重复b）和c）; 以及e）当在c）中各自的分类结果不匹配的样本数量已经降低到或低于预定值时，导致d）停止。

5. 发明申请

US20090222390A1 Method, program and apparatus for generating two-class classification/prediction model 有权
标题翻译：用于生成两类分类/预测模型的方法，程序和装置
公开(公告)号：US20090222390A1
公开(公告)日：2009-09-03
申请号：US12453247
申请日：2009-05-04
申请人： Kohtarou Yuta
发明人： Kohtarou Yuta
IPC分类号： G06N3/00 , G06N5/04
CPC分类号： G06F17/30412 , G06F17/30707 , G06F19/707 , G06N99/005
摘要： A method includes: a) preparing as training data a sample set that contains a plurality of samples belonging to a first class and a plurality of samples belonging to a second class; b) generating, by performing discriminant analysis on the sample set, a first discriminant function having a high classification characteristic for the first class and a second discriminant function having a high classification characteristic for the second class; c) by classifying the sample set using the first and second discriminant functions, isolating any sample whose classification results by the first and second discriminant functions do not match; d) forming a new sample set by grouping together any sample thus isolated, and repeating b) and c) by using the new sample set; and e) causing d) to stop when the number of samples each of whose classification results do not match in c) has decreased to or below a predetermined value.
摘要翻译：一种方法包括：a）准备包含属于第一类的多个样本和属于第二类的多个样本的样本集合作为训练数据; b）通过对样本集进行判别分析，生成第一类具有高分类特性的第一判别函数和对第二类具有高分类特性的第二判别函数; c）通过使用第一和第二判别函数对样本集进行分类，分离由第一和第二判别函数分类结果不匹配的任何样本; d）通过将所分离的样品分组在一起形成新的样品组，并通过使用新的样品组重复b）和c）; 以及e）当在c）中各自的分类结果不匹配的样本数量已经降低到或低于预定值时，导致d）停止。

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