waveform spectrum
Overview Sphinx4 is a pure Java speech recognition library. It provides a quick and easy API to convert the speech recordings into text with the help CMUSphinx acoustic models.
Prover9 Download
Prover9, Mace4, and several related programs come packaged in a system called LADR (Library for Automated Deduction Research). If you install one of these LADR packages, you will get command-line programs. (The programs are run by typing commands to a command prompt, terminal, or shell.) A GUI (graphical user interface) called Prover9-Mace4 is also available. (The GUI is self-contained, so there is no need to install one of these LADR packages to use the GUI.)
Support Vector Machines (SVM)
Support Vector Machines (SVM) Support Vector Machines (SVM) Introductory Overview Support Vector Machines are based on the concept of decision planes that define decision boundaries.
Conditional random field
Conditional random fields (CRFs) are a class of statistical modelling method often applied in pattern recognition and machine learning, where they are used for structured prediction. Whereas an ordinary classifier predicts a label for a single sample without regard to "neighboring" samples, a CRF can take context into account; e.g., the linear chain CRF popular in natural language processing predicts sequences of labels for sequences of input samples. CRFs are a type of discriminative undirected probabilistic graphical model. It is used to encode known relationships between observations and construct consistent interpretations.
[LIBSVM资料] 将UCI数据、Matlab数据转变为LIBSVM使用数据格式的程序
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About the Chi-Square Test
Generally speaking, the chi-square test is a statistical test used to examine differences with categorical variables. There are a number of features of the social world we characterize through categorical variables - religion, political preference, etc. To examine hypotheses using such variables, use the chi-square test.
Latent semantic analysis
Latent semantic analysis (LSA) is a technique in natural language processing, in particular in vectorial semantics, of analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms. LSA assumes that words that are close in meaning will occur in similar pieces of text. A matrix containing word counts per paragraph (rows represent unique words and columns represent each paragraph) is constructed from a large piece of text and a mathematical technique called singular value decomposition (SVD) is used to reduce the number of columns while preserving the similarity structure among rows. Words are then compared by taking the cosine of the angle between the two vectors formed by any two rows. Values close to 1 represent very similar words while values close to 0 represent very dissimilar words.[1]
Libsvm中svmtrain操作的输入参数问题？ - 机器学习
最近一直在看SVM参数寻优的问题，为了在实践中实现自己寻优，使用台湾大学林智仁开发的libsvm软件包中的matlab接口函数。对函数svmtrain中的 [, 'libsvm_options']进行了多次测试。但是遇到下面的两种情况令我很是费解，无奈提问请教知乎大神，希望得到好心人的解答，万分感激！<br> 为了方便描述问题，将c_val 和 g_val都设置成定值。注意参数中使用了-v 10，就是使用10折交叉验证。
Singular value decomposition
Visualization of the SVD of a two-dimensional, real shearing matrixM. First, we see the unit disc in blue together with the two canonical unit vectors. We then see the action of M, which distorts the disk to an ellipse. The SVD decomposes M into three simple transformations: an initial rotationV*, a scaling Σ along the coordinate axes, and a final rotation U. The lengths σ1 and σ2 of the semi-axes of the ellipse are the singular values of M, namely Σ1,1 and Σ2,2. Formally, the singular value decomposition of an m×n real or complex matrix M is a factorization of the form
Curse of dimensionality
The curse of dimensionality refers to various phenomena that arise when analyzing and organizing data in high-dimensional spaces (often with hundreds or thousands of dimensions) that do not occur in low-dimensional settings such as the three-dimensional physical space of everyday experience. The term curse of dimensionality was coined by Richard E. Bellman when considering problems in dynamic optimization.[1][2] The "curse of dimensionality" depends on the algorithm[edit]
[LibSVM] SVM 實驗最佳化參數
多媒體系統課當時已稍微玩過LibSVM實驗，然而那時候並沒有下參數，讓分類結果最佳化，於是今日捲土重來。 先不用理解複雜的LibSVM原理，跑出數字和視覺化結果，讓我們更感興趣！ 準備工作：
ODIN - The Online Database of Interlinear Text
ODIN, the O nline D atabase of In terlinear text, is a repository of Interlinear Glossed Text (IGT) extracted mainly from scholarly linguistic papers. The repository is both broad-coverage, in that it contains data for a variety of the world's languages (limited only by what data is available and what has been discovered), and rich, in that all data contained in the repository has been subject to linguistic analysis. IGT is a standard method within the field of linguistics for presenting language data, with (1) being a typical example.
[LibSVM] Support Vector Machine (SVM) 實驗
多媒體設計系統作業三是要我們實驗SVM， 老師給我們training.txt和testing.txt兩個檔案， 分別用來training和predicting。 在此之前我參考了以下的網頁： 以下步驟為我的作業專用，官方教學可看piaip’s Using (lib)SVM Tutorial，下面有官方實驗懶人包：P 1.到台大資工林智仁老師的SVM網頁 2.下載libsvm.zip 3.解開zip檔到此資料夾C:\ 4.將training.txt和testing.txt放入此資料夾C:\libsvm-3.0\windows 5.執行DOS模式，按WIN+R，跳出「執行」視窗，在「開啟」的地方輸入「cmd」 6.輸入「cd C:\libsvm-3.0\windows」7. 輸入指令svm-train training.txt如下：
Knowledge Engineering for NLP: Testsuite specifications
Navigation Preliminaries If your language uses non-ascii characters, you'll need to either: Settle on a system of transliteration (preferably both non-lossy and standard for the language, but at least the former) Figure out an input method for the standard orthography in emacs as well as how to create unicode files.
