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random_walk_3D
- Brownian motion. A random walk is similar to markov process, however a markov process has no memory, where a random walk uses an initial state. The next step in the walk does not necessarily depend on your current state but will be referenc
random_walk
- 2D version of Brownian motion. A random walk is similar to markov process, however a markov process has no memory, where a random walk uses an initial state. The next step in the walk does not necessarily depend on your current state but wil
Chapter2
- 随机过程中的高斯-马尔科夫过程，以及产生的概率密度函数-The Gaussian random process- Markov process, and the resulting probability density function
spider20060724
- 机器学习和模式识别工具包spider。内容很丰富。包含svm 决策树（C45,J48）、svm、knn、adaboost、bagging、hmm（隐马尔科夫模型）、随机树（random forest）等-Machine learning and pattern recognition toolkit spider. Very rich in contents. Tree contains svm (C45, J48), svm, knn, adaboost, bagging, hmm (hidd
libORF-master
- 针对各种机器学习，深度学习领域的一个matlab工具包-A machine learning library focused on deep learning.Following algorithms and models are provided along with some static utility classes: - Naive Bayes, Linear Regression, Logistic Regression, Softmax Regression, Linear S
hanlp-1.2.2-sources-
- hanlp源码，包括各种分词算法的实现，比如隐马尔科夫模型，条件随机场模型，N最短模型等，还有语义分析，情感分析等-hanlp source, including a variety of sub achieve segmentation algorithm, such as hidden Markov model, conditional random, N shortest models, as well as semantic analysis, sentiment analysis, e
Untitled
- 使用matlab编写的基于马尔科夫链的随机游走过程，产生推荐列表-Using matlab prepared based on Markov chain random walk process, resulting in a recommendation list
2012.李航.统计学习方法
- 《统计学习方法》是计算机及其应用领域的一门重要的学科。《统计学习方法》全面系统地介绍了统计学习的主要方法，特别是监督学习方法，包括感知机、k近邻法、朴素贝叶斯法、决策树、逻辑斯谛回归与最大熵模型、支持向量机、提升方法、EM算法、隐马尔可夫模型和条件随机场等。除第1章概论和最后一章总结外，每章介绍一种方法。叙述从具体问题或实例入手，由浅入深，阐明思路，给出必要的数学推导，便于读者掌握统计学习方法的实质，学会运用。为满足读者进一步学习的需要，书中还介绍了一些相关研究，给出了少量习题，列出了主要参考文
VisualElements
- The properties of IsohyetoseNep depend greatly on the assumptions inherent in our methodology; in this section, we outline those assumptions. Any robust deployment of perfect models will clearly require that Boolean logic and Internet QoS are mostly
hsmm
- 隐马尔科夫模型是关于时序的概率模型，描述由一个隐藏的马尔科夫链随机生成不可观测的状态随机序列，再由各个状态生成一个观测而产生观测序列的过程。隐藏的马尔科夫链随机生成的状态的序列，称为状态序列；每个状态生成一个观测，而由此产生的观测的随机序列，称为观测序列。马尔科夫链由初始概率分布、状态转移概率分布以及观测概率分布确定(The hidden Markov model is a probabilistic model for time series. It describes the process
rwm
- 用random walk metropolis算法计算标准正态分布的收敛(Using Markov Chain Monte Carlo Method to Calculate the Convergence of Standard Normal Distribution)