supervised classification

The main contributions include: 1) A novel dimension reduction method, Supervised Latent Semantic Indexing ( SLSI), was proposed to represent documents for text classification tasks.

第一，提出一种有监督的潜在语义索引（SLSI）模型降维方法，用于文本分类任务中的特征表示。

Clustering index method supervised by semantic class and Bayes-based semantic video classification method: A perfect video database organization should be that video feature vectors, which are not only semantic relevant but also their visual features themselves similar, are stored continuously.

基于语义监督的特征聚类索引方法和基于Bayes的视频语义分类方法：理想的视频库组织方法应该把语义相关并且特征相似的视频的特征向量相邻存储。

A remote sensing image supervised classification method based on spectral and textural features is proposed.

提出了一种基于颜色和纹理特征的遥感图像监督分类法。

Classification accuracy of unsupervised learning algorithm is usually not satisfactory, so people tend to make use of supervised learning in practice.

由于无监督学习的准确率通常不能令人满意，在实际应用中人们趋向于运用有监督方法。

The Analysis on The Comparison between Parallelepiped Supervised Classification and Artificial Identification on The Remote Sensing Image

遥感影像平行六面体法则监督分类与目视识别的对比分析

For learning document classification on line, the paper gives the semi-supervised learning fuzzy ART model ( SLFART) based on adaptive resonance theory and the models algorithm.

为了对在线学习文档进行分类，本文根据自适应谐振理论给出了一个半监督学习模糊ART模型（SLFART）及其算法。

Our proposed CAAR algorithm is applied to supervised classification of image content and large-scale data mining, which is very effective.

将CAAR分类新技术应用于有监督的图像内容分类学习和大规模数据机器学习取得了很好的效果。

Using Landsat and CBERS images as two data sources, each pixel was dealt as a mixture of vegetation, high-and low-albedo and bare soil, impervious surface was extracted with linear spectral mixture model, landuse information was determined with supervised classification method.

采用Landsat、中巴资源卫星两种遥感影像数据，每个像元均可视为由高反照度地物、低反照度地物、植被和裸土组成，运用线性光谱混合模型提取不透水面信息。

Our proposed cross-domain transfer learning method for utilizing web multimedia objects without true labels in performing supervised classification tasks.

该交叉域的迁移学习的方法可以通过未标注真实类别标签的网络多媒体数据完成有监督分类的学习任务。