Description: 基于多分类器组合的笔迹验证 －－文章-Based on the composition of the classifier based on the handwriting test multiple classifiers combination of handwriting authentication -- article Platform: |
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Author:Yuan |
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Description: 基于多分类器组合的笔迹验证 －－文章-Based on the composition of the classifier based on the handwriting test multiple classifiers combination of handwriting authentication-- article Platform: |
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Author:Yuan |
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Description: 目的:运用强化学习!多分类器集成!降维方法等最新计算机技术,结合细胞病理知识,设计制作/智能化肺癌细胞病理图像诊断系统0"方法:采集细胞图像,运用基于强化学习的图像分割法将细胞区域从背景中分离出来 运用基于样条和改进2方法对重叠细胞进行分离和重构 提取40个细胞特征用于贝叶斯!支持向量机!紧邻和决策树4种分类器,集成产生肺癌细胞分类结果 建立肺癌细胞病理图库,运用基于等降维方法对细胞进行比对,给予未定型癌细胞分类"结果:/智能化肺癌细胞病理诊断系统0应用于临床随机1200例肺部病灶穿刺细胞学涂片,肺癌识别诊断率94180 ,假阳性率1185 ,假阴性率3135 ,肺癌分类识别率82190 ,核异型细胞识别率74120 "结论:/智能化肺癌早期细胞病理诊断系统0对肺癌细胞涂片诊断率高,克服了肺癌细胞病理诊断过程中取检细胞数量少,重叠细胞识别率低,涂片背景及染色差异等干扰因素,可辅助临床肺部病灶的穿刺细胞病理诊断"-Objective Design and develop a intelligent cytopathological lung cancer diagnosing system(ICLCDS) utilizing the latest computer technologies(including Reinforcement Lcaming Multiple Classifier Fusion and Dimcnsionality Reduction) and the cy-topathological knowledge on lung canccrcclls Methods We got information ofcclls and segregated cell regions in a slice image using an magi scgmcntouon a址orithm Sascd on reinforcement lcaming including rcconstmction of overlapped cell area Sascd on B一Spline and improved dcBoor-Cox Mcthoc} We comSincd multiple classifiers including Baycsian classific:Support Vector Machine(SVM)
classific K-Ncarcst NcighSour( KNN) and Decision c classific to achieve an accurate result of cytopathological lung cancer diag-nosis Results Experimental results on 1 200 cases randomly selected we as follows the accurate diagnosis rate for lung cancer idcn-tification was the false positive rate was 1. 8`J /c‘the false negative rate was 3. 3`J /c‘the type class Platform: |
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Author:高阳 |
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Description: 多分类支持向机C语言源代码.内含祥细使用说明及应用例子与数据.-Multiple classifiers to support the C language source code machine.祥细contains examples of use and application and data. Platform: |
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Author:zhangql |
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Description: 多分类器集成系统是当前机器学习领域的一个研究热点。由于使用多个基分类器构建的集成系统通常比单个优秀的分类器具有更强的泛化能力，因此多分类器集成系统为许多基于传统模式识别方法很难解决的分类问题提供了新的解决方案。
-Integration of multiple classifier machine learning system is currently a hot research field. The use of multiple base classifiers built than a single integrated system is usually good classifier has greater generalization ability of the integration of multiple classifier systems for many of the traditional pattern recognition method based on the classification of problems difficult to solve a new solution program. Platform: |
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Author:dreamer |
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Description: Applied Statistics Using SPSS,STATISTICA,MATLAB and R
Joaquim P.
ISBN 978-3-540-71971-7 Springer Berlin Heidelberg New York-Applied Statistics Using SPSS, STATISTICA,MATLAB and R
Inclusion of R as an application tool. As a matter of fact, R is a free
software product which has nowadays reached a high level of maturity
and is being increasingly used by many people as a statistical analysis
tool.
Chapter 3 has an added section on bootstrap estimation methods, which
have gained a large popularity in practical applications.
A revised explanation and treatment of tree classifiers in Chapter 6 with
the inclusion of the QUEST approach.
Several improvements of Chapter 7 (regression), namely: details
concerning the meaning and computation of multiple and partial
correlation coefficients, with examples a more thorough treatment and
exemplification of the ridge regression topic more attention dedicated to
model evaluation. Platform: |
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Author:ABC |
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Description: Abstract—In recent years, the use of machine learning
algorithms (classifiers) has proven to be of great value in
solving a variety of problems in software engineering including
software faults prediction. This paper extends the idea of
predicting software faults by using an ensemble of classifiers
which has been shown to improve classification performance in
other research fields. Benchmarking results on two NASA
public datasets show all the ensembles achieving higher
accuracy rates compared with individual classifiers. In
addition, boosting with AR and DT as components of an
ensemble is more robust for predicting software faults.
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Author:kanly88 |
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Description: 将多类别问题分解成多个二类别问题是解决多类别分类问题的常用方式。传统one against all（OAA）分解方式的性能更多的依赖于个体分类器的精度，而不是它的差异性。本文介绍一种基于集成学习的适于多类问题的神经网络集成模型，其基本模块由一个OAA方式的二类别分类器和一个补充多类分类器组成。测试表明，该模型在多类问题上比其他经典集成算法有着更高的精度，并且有较少存储空间和计算时间的优势。-Decompose multi-class problem into multiple binary class problems is a common way to solve multi-class problem. The performance of the traditional one against all (OAA) decomposition way mainly depends on the accuracy of individual classifiers, not their diversity. In this paper, a new ensemble learning model applicable to multiclass domains is proposed. The proposed model is a neural network ensemble in which the base learners are composed by the union of a binary classifier and a complement multi-class classifier. Experimental results show that our model has higher accuracy than other classical ensemble learning for multi-class problems. And it has the superiority with less storage space and computation time. Platform: |
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Author:刘茂 |
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Description: Many state-of-the-art approaches for object recognition
reduce the problem to a 0-1 classifi cation task. Such re-
ductions allow one to leverage sophisticated classifi ers for
learning. These models are typically trained independently
for each class using positive and negative examples cropped
from images. At test-time, various post-processing heuris-
tics such as non-maxima suppression (NMS) are required
to reconcile multiple detections within and between differ-
ent classes for each image. Though crucial to good perfor-
mance on benchmarks, this post-processing is usually de-
fi ned heuristically.-Many state-of-the-art approaches for object recognition reduce the problem to a 0-1 classification task. Such re-ductions allow one to leverage sophisticated classifiers for learning. These models are typically trained independently for each class using positive and negative examples cropped from images. At test-time, various post-processing heuris-tics such as non-maxima suppression (NMS) are required to reconcile multiple detections within and between differ-ent classes for each image. Though crucial to good perfor-mance on benchmarks, this post-processing is usually de-fined heuristically. Platform: |
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Author:xukaijun |
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Description: SVM SMO 多分类的c++源码，自己编的，可以运行-SVM SMO multiple classifiers c++ of source code, own series, you can run Platform: |
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Description: 可以集成多个分类器的投票算法，采用python实现(The voting algorithm of multiple classifiers can be integrated and implemented by python.) Platform: |
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Author:若雨寒暄 |
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Description: 针对不平衡样本的，可以综合多个分类器的投票算法。(For the unbalanced samples, the voting algorithm of multiple classifiers can be synthesized.) Platform: |
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Author:若雨寒暄 |
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Description: 粒子群优化算起源于对鸟群、鱼群以及对某些社会行为的模拟，是一种基于群体智能的进化计算技术。而小生境技术则起源于遗传算法，这种方法能使基于群体的随机优化算法形成物种，从而使相应的优化算法具有发现多个最优解的能力。而多分类器集成技术则是通过多个分类器进行某种组合来决定最终的分类，以取得比单个分类器更好的性能。多分类器集成技术要求基元分类器不仅个体性能要好并且其差异度要大，这与小生境技术形成物种的能力具有很多内在的相似性。目前己经有研究者将小生境技术应用于多分类器集成，但由于传统的小生境技术仍然不完善，存在一些内在的陷，因而这些应用还不成熟和完善。
(Particle swarm optimization (partieleSwarmOptimization) originated in the birds, fish, and of a
Some simulation of social behavior, is a swarm intelligence-based evolutionary computing. The origin of the niche technology is In genetic algorithms, this method can make random optimization algorithm based on the formation of groups of species, so that the appropriate priority
Algorithm has the ability to find multiple optimal solutions. The integration technology of multiple classifiers is through multiple classifiers into
Some combination of the line to determine the final classification, in order to obtain better than a single classifier performance. Integration of multiple classifiers
Technical requirements for primitive classification is not only better individual performance and the difference to a large degree, which form a niche technology The ability of species has many inherent similarities. The researchers will now have a niche technology used in multisection
Class ens) Platform: |
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Author:dreamer |
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Search - multiple classifiers