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Bluebit.gr - Bluebit - Όλα τα νÎα για τις ΕλληνικÎÏ‚ σειÏÎÏ‚ στην τηλεόÏαση
Το bluebit.gr παÏουσιάζει όλες τις εξελίξεις από κάθε ελληνική σειÏά που παίζεται ή Ï€Ïόκειται να παιχτεί στα Ελληνικά τηλεοπτικά κανάλια. Επικοινωνήστε μαζί μας: info@bluebit.gr
SINGULAR VALUE Decomposition SVD
Example with O-matrix /Mathlab O>(u,s,v)=svd(x) O>x { [ 1 , 2 ] [ 2 , 3 ] [ 3 , 4 ] } O>[u,s,v]=svd(x) O>u { [ -0.338098 , 0.847952 , 0.408248 ] [ -0.550649 , 0.173547 , -0.816497 ] [ -0.763201 , -0.500857 , 0.408248 ] } O>s { [ 6.54676 , 0 ] [ 0 , 0.374153 ] [ 0 , 0 ] } O>v { [ -0.569595 , -0.821926 ] [ -0.821926 , 0.569595 ] } O>[e,d]=eigen(x'x) O>[e,d]=eigen(x'*x) O>e { [ (0.569595,0) , (-0.821
H2 Database Engine
Welcome to H2, the free SQL database. The main feature of H2 are: It is free to use for everybody, source code is included Written in Java, but also available as native executable JDBC and (partial) ODBC API Embedded and client/server modes Clustering is supported A web client is included No Javascript If you are not automatically redirected to the main page, then Javascript is currently disabled
TinySVM: Support Vector Machines
TinySVM is an implementation of Support Vector Machines (SVMs) [Vapnik 95], [Vapnik 98] for the problem of pattern recognition. Support Vector Machines is a new generation learning algorithms based on recent advances in statistical learning theory, and applied to large number of real-world applications, such as text categorization, hand-written character recognition. List of Contents What's new Fe
gboost - Graph Boosting Toolbox for Matlab
The gboost toolbox is a framework for classification of connected, undirected, labeled graphs. A typical graph is shown on the right: each node and edge is labeled by a discrete value. The gboost classifiers check for the presence of certain subgraphs in the larger graph. The subgraphs being checked are optimally determined by discriminative subgraph mining. The classification hypotheses is interp
SVD decomposition - C++, C#, Java library
SVD decomposition The singular value decomposition of MxN matrix A is its representation as A = U W VT, where U is an orthogonal MxM matrix, V - orthogonal NxN matrix. The diagonal elements of matrix W are non-negative numbers in descending order, all off-diagonal elements are zeros. The matrix W consists mainly of zeros, so we only need the first min(M,N) columns (three, in the example above) of
JAMA: Java Matrix Package
Background JAMA is a basic linear algebra package for Java. It provides user-level classes for constructing and manipulating real, dense matrices. It is meant to provide sufficient functionality for routine problems, packaged in a way that is natural and understandable to non-experts. It is intended to serve as the standard matrix class for Java, and will be proposed as such to the Java Grande For
Latent semantic analysis - Wikipedia
Latent semantic analysis (LSA) is a technique in natural language processing, in particular distributional 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 (the distributional hypothesis). A matrix contai
0.1 0.1.1 Latent Semantic Indexing Latent Semantic Indexing :LSI ã®æ¬¡å…ƒã¾ã¨ã‚ã¦æ¬¡å…ƒã‚’下ã’ã‚‹ã¨ã„ã†ä½œæ¥ã‚’è¡Œ ãªã†ã‚ã‘ã§ã‚る。 ã•ã¦ã€æ¬¡å…ƒã‚’下ã’ã‚‹ã¨ã„ã†ã“ã¨ã¯å½“ç„¶æƒ…å ± ã®æ失を伴ã†ã‚ã‘ã ãŒã€ã“ã®æ�
0.1 0.1.1 Latent Semantic Indexing Latent Semantic Indexing :LSI ã®æ¬¡å…ƒã¾ã¨ã‚ã¦æ¬¡å…ƒã‚’下ã’ã‚‹ã¨ã„ã†ä½œæ¥ã‚’è¡Œ ãªã†ã‚ã‘ã§ã‚る。 ã•ã¦ã€æ¬¡å…ƒã‚’下ã’ã‚‹ã¨ã„ã†ã“ã¨ã¯å½“ç„¶æƒ…å ± ã®æ失を伴ã†ã‚ã‘ã ãŒã€ã“ã®æ失を最å°é™ã« ãã„ã¨ã‚ã‚‹ãŸã‚ã«æœ€å°äºŒä¹—誤差 ã¨ã„ã†è€ƒãˆæ–¹ ã§æœ›ã‚€ã€‚ 2 ã¨ã¯ ベクトル空間モデルã§ã¯ã‚¿ãƒ¼ãƒ ã®ç”Ÿèµ·ãŒç‹¬ ç«‹ã§ã‚ã‚‹ã“ã¨ã‚’仮定ã—ã¦ã€å„タームを一ã¤ã® 次元ã«å¯¾å¿œã•ã›ã‚‹ãƒ™ã‚¯ãƒˆãƒ«ç©ºé–“を作ã£ãŸã€‚ã— ã‹ã—ã€å®Ÿéš›ã«ã¯ã‚¿ãƒ¼ãƒ é–“ã®ç‹¬ç«‹æ€§ã¯ä¿è¨¼ã•ã‚Œ ãªã„。例ãˆã°ã€æƒ…å ±æ¤œç´¢ã®åˆ†é‡Žã§ã¯ã€Œãƒ™ã‚¯ãƒˆ ルã€ã¨ã€Œç©ºé–“ã€ã¯é«˜ã„相関をæŒã¤ã“ã¨ã¯å®¹æ˜“ ã«äºˆæƒ³ã§ãる。ã“ã®å•é¡Œã¸ã®å¯¾ç–ã¨ã—ã¦ã¯ä»¥ 下㮠2 点ãŒé‡è¦ã§ã‚る。 0.1.2 特異値分解 LSI 㧠用 ã„ ã‚‹ æ•° å¦ çš„ 㗠㋠㑠㯠特 ç•° 値 分 解 (Singular Value D
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