搜索资源列表
facerecognition
- 采用OpenCV进行人脸识别，隐马尔科夫链的应用，由混合分量来分割HMM的每个内在状态的所有观测值，运用现有的图像观测值分割为所有嵌入和内部的HMM函数，计算可能的变换矩阵-Using OpenCV for face recognition, hidden Markov chain applications, from the mixed components to split the internal state of each HMM all observations, use of the
nlu_project
- 采用机器学习的方法进行自然语言处理，对中文进行分词和词性标注。分词采用crf模型，词性标注用hmm模型，解码算法为Vertibi算法。本系统使用java语言编写-Using machine learning methods for natural language processing, carried out on the Chinese word segmentation and POS tagging. Segmentation using crf model, tagging with
menmang_v58
- 可以提取一幅图中想要的目标，粒子图像分割及匹配均为自行编制的子例程，完整的基于HMM的语音识别系统。- Target can be extracted in a picture you want, Particle image segmentation and matching subroutines themselves are prepared, Complete HMM-based speech recognition system.
nengqeng_v53
- 中介真值程度度量，基于中介真值程度度量的图像分割完整的基于HMM的语音识别系统，基于负熵最大的独立分量分析。- The true extent of the value of the intermediary measure, measure the true extent of the agency based on the value of image segmentation Complete HMM-based speech recognition system, Based on ne
siesing_v50
- 完整的基于HMM的语音识别系统，粒子图像分割及匹配均为自行编制的子例程，DSmT证据推理的组合公式计算函数。- Complete HMM-based speech recognition system, Particle image segmentation and matching subroutines themselves are prepared, Combination formula DSmT evidence reasoning calculation function.
CWSS17.1.1.4
- 基于隐马尔科夫模型的中文分词系统，上交ieee专业大一作业，界面一般，主要用于学习，在此分享，注：开发环境python3.5(Based on Hidden Markov model of Chinese word segmentation system, on the IEEE professional freshman job, interface is common, mainly used for learning, in this share, note: development en
24.HMM
- 通过hmm实现中文分词，并且能自动发现新词的功能(The Chinese word segmentation is realized by HMM, and the function of new words can be automatically found)