Location:
Search - som brain
Search list
Description: MRI Brain Tumour Classification - SOM ( Self Organized Map)-MRI Brain Tumour Classification- SOM ( Self Organized Map)
Platform: |
Size: 371712 |
Author: sunda |
Hits:
Description: this was the code for SOM based brain image segmentation which can segment brain image and finally the segmented labelled image was represent as a color label. this code segment the tumor spot of an given brain image and represent as a color label. we can use T1 and T2 weighted brain image.
Platform: |
Size: 1131520 |
Author: vasanth |
Hits:
Description: SOM神经网络，Self-Organizing Map 的缩写，即自组织映射。
1981年芬兰Helsink大学的T.Kohonen教授提出一种自组织特征映射网，简称SOM网，又称Kohonen网。 Kohonen认为：一个神经网络接受外界输入模式时，将会分为不同的对应区域，各区域对输入模式具有不同的响应特征，而且这个过程是自动完成的。自组织特征映射正是根据这一看法提出来的，其特点与人脑的自组织特性相类似。
由于它的强大功能，多年来，神经网络在数据分类、知识获取、过程监测、故障识别等领域中得到广泛运用。-SOM neural network, Self-Organizing Map acronym, that self-organizing map. 1981 University Professor T.Kohonen Finland Helsink propose a self-organizing feature map network, referred to as SOM network, also known as Kohonen network. Kohonen think: when a neural network to outside input mode, will be divided into different corresponding regions, the regional input to the model with different response characteristics, and this process is done automatically. It is similar to the self-organizing map according to this view put forward, which is characterized by self-organization and phase characteristics of the human brain. Because of its powerful over the years, neural networks in data classification, knowledge acquisition, process monitoring, fault identification and other areas to be widely used.
Platform: |
Size: 1024 |
Author: |
Hits:
Description: SOM神经网络：Self-Organizing Map 的缩写，即自组织映射。
1981年芬兰Helsink大学的T.Kohonen教授提出一种自组织特征映射网，简称SOM网，又称Kohonen网。 Kohonen认为：一个神经网络接受外界输入模式时，将会分为不同的对应区域，各区域对输入模式具有不同的响应特征，而且这个过程是自动完成的。自组织特征映射正是根据这一看法提出来的，其特点与人脑的自组织特性相类似。
由于它的强大功能，多年来，神经网络在数据分类、知识获取、过程监测、故障识别等领域中得到广泛运用。-SOM Neural Networks: Self-Organizing Map acronym, that self-organizing map. 1981 University Professor T.Kohonen Finland Helsink propose a self-organizing feature map network, referred to as SOM network, also known as Kohonen network. Kohonen think: when a neural network to outside input mode, will be divided into different corresponding regions, the regional input to the model with different response characteristics, and this process is done automatically. It is similar to the self-organizing map according to this view put forward, which is characterized by self-organization and phase characteristics of the human brain. Because of its powerful over the years, neural networks in data classification, knowledge acquisition, process monitoring, fault identification and other areas to be widely used.
Platform: |
Size: 1024 |
Author: |
Hits:
Description: 一个神经网络接收外界输入模式时，将会分为不同的对应区域，各区域对输入模式有不同的响应特征，而这个过程是自动完成的。其特点与人脑的自组织特性类似。SOM的目标是用低维（通常是二维或三维）目标空间的点来表示高维空间中的所有点，尽可能地保持点间的距离和邻近关系（拓扑关系）。(When a neural network receives external input mode, it will be divided into different corresponding regions. Each region has different response characteristics to input mode, and this process is completed automatically. Its characteristics are similar to the self - organization characteristics of the human brain. The goal of SOM is to express all points in high dimensional space with the point of low dimensional (usually two-dimensional or three-dimensional) target space, and keep the distance between points and adjacent relations (Topological Relations) as much as possible.)
Platform: |
Size: 1024 |
Author: 安联的大球童 |
Hits:
Description: Kohonen and K-Means Algorithm used to used to segmentating the medical images
Platform: |
Size: 246006 |
Author: praba82 |
Hits: