• 针对自然图像纹理复杂特点,提出一种多种信息融合监督边界检测方法

    For natural images of complex texture, a supervised boundary detection method using the multi-information fusion was proposed.

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  • 两种方法获得监督实践经验

    There are two ways to gain supervised practice experience.

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  • 常用的分类方法监督分类监督分类。

    The normal ways are Surpervised classification and Un-supervised classification.

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  • 一种新颖监督学习方法

    Lattice machine is a novel approach to supervised learning.

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  • 是否证据表明方法监督交货完成情况适用纠正措施

    Is there evidence of a system to monitor the delivery performance and applicable corrective actions?

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  • 产品评价对象提取分析方法主要分为监督机器学习方法、半监督的机器学习算法监督的机器学习算法

    The method of product feature extraction and analysis can be divided into supervised machine learning methods, semi-supervised machine learning algorithms and unsupervised machine learning algorithm.

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  • 提出一种模式识别方法效地用于监督连续语音识别、文稿理解数据文件分析

    This paper raised a method of pattern recognition, it can be used to the continuous speech recognition with reference, text understanding and data file analysis.

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  • 分水岭分割基础,利用子区域像素值的高斯统计性质提出了监督图像背景学习方法

    A supervised image segmentation algorithm is proposed, which is based on Gaussian statistical property of sub-regions obtained by watershed segmentation.

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  • 第一,提出有监督潜在语义索引(SLSI)模型方法用于文本分类任务中的特征表示

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

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  • 用于油气预测方法基本上可分成:一类监督预测方法,另一类是无监督预测方法

    The methods used for oil and gas prediction can be classified as two kinds on the whole.

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  • 针对这个缺点提出了一种改进的、基于自适应最近邻法的局部线性嵌入方法,数值实验证明算法对于有监督学习问题,具较好的适应性

    An adaptive nearest neighbor locally linear embedding algorithm is proposed to overcome this shortage. Experiment results show that the algorithm ADAPTS well the supervised learning problems.

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  • 针对这个缺点提出了一种改进的、基于自适应最近邻法的局部线性嵌入方法,数值实验证明算法对于有监督学习问题,具较好的适应性

    An adaptive nearest neighbor locally linear embedding algorithm is proposed to overcome this shortage. Experiment results show that the algorithm ADAPTS well the supervised learning problems.

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