Search - Information Gain

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[matlab] @linear DL : 0: 针对SVM法线特征筛选算法仅考虑法线对特征筛选的贡献,而忽略了特征分布对特征筛选的贡献的不足,在对SVM法线算法进行分析的基础上,基于特征在正、负例中出现概率的不同提出了加权SVM法线算法,该算法考虑到了法线和特征的分布.通过试验可以看出,在使用较小的特征空间时,与SVM法线算法和信息增益算法相比,加权SVM法线算法具有更好的特征筛选性能.-Normal feature selection for SVM algorithm only considered normal for the contribution of feature selection, to the neglect of the characteristics of the distribution of feature selection have contributed to the lack of normal SVM algorithm based on the analysis, based on the characteristics of the positive and negative cases emergence of a different probability-weighted normal SVM algorithm, which takes into account the distribution and characteristics of normal. through the test can be seen in the use of smaller feature space, the normal and the SVM algorithm and information gain algorithm, normal weighted SVM algorithm has better performance of feature selection.
Date : 2025-12-31 Size : 4kb User : 苏苏
[matlab] FPID_control_Identified_Model_V DL : 0: 实际系统辨识高增益一阶系统的PID型模糊控制，可是小论文的资料！欢迎交流！-Identification of the actual system of high-gain first-order system PID fuzzy control, but little information on the paper! Welcome to exchange!
Date : 2025-12-31 Size : 2kb User : zhuyinghe
[matlab] smithchart DL : 0: The Smith chart, invented by Phillip H. Smith (1905-1987),[1][2] is a graphical aid or nomogram designed for electrical and electronics engineers specializing in radio frequency (RF) engineering to assist in solving problems with transmission lines and matching circuits.[3] Use of the Smith chart utility has grown steadily over the years and it is still widely used today, not only as a problem solving aid, but as a graphical demonstrator of how many RF parameters behave at one or more frequencies, an alternative to using tabular information. The Smith chart can be used to represent many parameters including impedances, admittances, reflection coefficients, scattering parameters, noise figure circles, constant gain contours and regions for unconditional stability.[4][5] The Smith chart is most frequently used at or within the unity radius region. However, the remainder is still mathematically relevant, being used, for example, in oscillator design and stability analysis.-The Smith chart, invented by Phillip H. Smith (1905-1987),[1][2] is a graphical aid or nomogram designed for electrical and electronics engineers specializing in radio frequency (RF) engineering to assist in solving problems with transmission lines and matching circuits.[3] Use of the Smith chart utility has grown steadily over the years and it is still widely used today, not only as a problem solving aid, but as a graphical demonstrator of how many RF parameters behave at one or more frequencies, an alternative to using tabular information. The Smith chart can be used to represent many parameters including impedances, admittances, reflection coefficients, scattering parameters, noise figure circles, constant gain contours and regions for unconditional stability.[4][5] The Smith chart is most frequently used at or within the unity radius region. However, the remainder is still mathematically relevant, being used, for example, in oscillator design and stability analysis.
Date : 2025-12-31 Size : 2kb User : hazhiriq200
[matlab] matlab_Lab3 DL : 1: information gain calculation in Matlab
Date : 2025-12-31 Size : 1kb User : Molinken
[matlab] ParInfoGain DL : 0: ParInfoGain - Computes parallel information gain and gain ratio in Matlab using the Matlab Parallel Computing Toolbox or the Distributed Server (if available) Information gain is defined as: InfoGain(Class,Attribute) = H(Class) - H(Class | Attribute) Gain ratio is defined as: GainRatio(Class, Attribute) = (H(Class) - H(Class | Attribute)) / H(Attribute). ...where H is the entropy, defined as - sum(i=1 to k) pi log2 pi-ParInfoGain - Computes parallel information gain and gain ratio in Matlab using the Matlab Parallel Computing Toolbox or the Distributed Server (if available) Information gain is defined as: InfoGain(Class,Attribute) = H(Class) - H(Class | Attribute) Gain ratio is defined as: GainRatio(Class, Attribute) = (H(Class) - H(Class | Attribute)) / H(Attribute). ...where H is the entropy, defined as - sum(i=1 to k) pi log2 pi
Date : 2025-12-31 Size : 3kb User : agkj
[matlab] kaerman DL : 0: 实现卡尔曼滤波，可以看出，滤波过程是以不断地“预测—修正”的递推方式进行计算，先进行预测值计算，再根据观测值得到的新信息和kalman 增益（加权项），对预测值进行修正。由滤波值可以得到预测，又由预测可以得到滤波，其滤波和预测相互作用，并不要求存储任何观测数据，可以进行实时处理。-Kalman filtering, can be seen, the filtering process is constantly " forecast- Fixed" recursive manner calculated to predict the value of the first, and then according to the new information should be observed and the kalman gain (weighted items), on predictive value of the amendment. Value can be predicted by the filter, but also can be filtered by the forecast, and its interaction filtering and prediction, does not require the storage of any observational data, real-time processing.
Date : 2025-12-31 Size : 1kb User : 孙芳
[matlab] weinalvbo DL : 0: 可以看出，滤波过程是以不断地“预测—修正”的递推方式进行计算，先进行预测值计算，再根据观测值得到的新信息和kalman 增益（加权项），对预测值进行修正。由滤波值可以得到预测，又由预测可以得到滤波，其滤波和预测相互作用，并不要求存储任何观测数据，可以进行实时处理。-It can be seen, the filtering process is constantly " forecast- Fixed" recursive manner calculated to predict the value of the first, and then according to the new information should be observed and the kalman gain (weighted items), on the predictive value of the amendment. Value can be predicted by the filter, but also can be filtered by the forecast, and its interaction filtering and prediction, does not require the storage of any observational data, real-time processing.
Date : 2025-12-31 Size : 2kb User : 孙芳
[matlab] C4_5.m DL : 0: his algorithm was proposed by Quinlan (1993). The C4.5 algorithm generates a classification-decision tree for the given data-set by recursive partitioning of data. The decision is grown using Depth-first strategy. The algorithm considers all the possible tests that can split the data set and selects a test that gives the best information gain. For each discrete attribute, one test with outcomes as many as the number of distinct values of the attribute is considered. For each continuous attribute, binary tests involving every distinct values of the attribute are considered. In order to gather the entropy gain of all these binary tests efficiently, the training data set belonging to the node in consideration is sorted for the values of the continuous attribute and the entropy gains of the binary cut based on each distinct values are calculated in one scan of the sorted data. This process is repeated for each continuous attributes.
Date : 2025-12-31 Size : 2kb User : rajesh
[matlab] tttttttttlpc.m DL : 0: 利用matlab的lpc function 實做出將input source音檔算出其coefficent and gain並由上述資料可以重新合成還原音訊-The lpc function using matlab real audio file to make the input source is calculated by its coefficent and gain reduction above information can be re-synthesized audio
Date : 2025-12-31 Size : 1kb User : hsujia
[matlab] TextureClassification_NonExtensiveEntropy DL : 0: Non-Extensive entropy with Gaussian Information Gain for identifying and classifying regular textures which contain repetitive patterns correlated over space that translates to high probabilities in the gray level co-occurrence matrix-Non-Extensive entropy with Gaussian Information Gain for identifying and classifying regular textures which contain repetitive patterns correlated over space that translates to high probabilities in the gray level co-occurrence matrix
Date : 2025-12-31 Size : 2kb User : lm1756
[matlab] ID3 DL : 0: MATLAB下的决策树ID3算法，应用信息增益来划分节点-ID3 decision tree algorithm under MATLAB application information gain to divide the node
Date : 2025-12-31 Size : 3kb User : hyhy