2. The Encoder-Decoder Model. 3. Relation between a SOM and Noisy Encoder-
Decoder. 4. Voronoi Tessellation. 5. Learning Vector Quantization (LVQ) ...
3. The Encoder-Decoder Model. 4. Relation between a SOM and Noisy Encoder-
Decoder. 5. Voronoi Tessellation. 6. Learning Vector Quantization (LVQ) ...
The experiments and evaluations of the proposed method have been performed using the NSL-KDD 99 intrusion detection dataset. Hybrid (LVQ_kNN) was able ...
proses pelatihan data menggunakan metode Learning Vector Quantization ....
Prinsip kerja dari algoritma LVQ adalah pengurangan node-node tetangganya.
Nov 27, 2013 - Since electronic data sets increase rapidly with respect to size and complexity ... Prototype-based methods enjoy a wide popularity in various application ... excellent generalization ability in the standard intermediate case, see e.g.
Tipping, 2000), or adaptive ridge regression and the incorporation of penalizing function as proposed in. Grandvalet (1998), Roth (2001), and Tibshirani (1996).
Apr 29, 2009 ... Learning vector Quantization (LVQ) is a neural net that combines competitive
learning with supervision. It can be used for pattern classification.
Oct 22, 2010 - Keywords: classification, Learning Vector Quantization, prototype based classifiers, similarity ...... [26] S. Seo and K. Obermayer. Soft learning ...
learning tools which are available today such as the support vector machine act ... allow a Euclidean representation of data at all, rather, data are given implicitly ..... Seo S. and Obermayer K.: Dynamic Hyperparameter Scaling Method for LVQ.
pendicitis, Australian, Breast Cancer Wisconsin, Glass, Heart, Ionosphere, LED (with. 500 generated samples), Ljubljana Breast Cancer, Voting and Wine, plus ...
Jun 26, 2009 - Matrix relevance learning has been introduced in [3, 4] as a ...... [8] S. Seo, K. Obermayer, Soft Learning Vector Quantization, Neural ...
data training. 4. Sulit untuk menentukan jumlah codebook vek- tor untuk masalah
yang diberikan. Algoritma LVQ(Fausett, 1994) : 0 . Inisialisasi vektor referensi.
Video, an important part of the Big Data initiative, is believed to contain the richest ... tion of all the data points
training. In comparison to the weighted Euclidean metric used in RLVQ ... Seo & Obermayer, 2003) resp. the discrete limit case shows poor results also in ..... thus, the number of free parameters of the GMLVQ network does not occur explicitly ...
3 Robust Soft Learning Vector Quantization. As proposed by Seo and Obermayer [9] RSLVQ is a generic algorithm in which different assumptions on the ...
Mar 5, 2013 - the-art machine learning tools such as the SVM occurs: they act as black- boxes. ...... Note that the data sets Voting, FaceRec, Sonatas, and Amazon 47 are almost. Euclidean, while all others ..... [38] S. Seo and K. Obermayer.
using the NSL-KDD99 network anomaly intrusion detection dataset. ... subset. The NSL KDD dataset includes a wide variety of intrusions together with normal activities simulated in a ..... http://www.ft.unicamp.br/RedesComplexas/downloads/ ...
100 square meter pressure-sensitive floor (EMFi floor) was recently installed in the ... Hidden Markov Models and Nearest-Neighbor clas- sification have been ...
and gives as output a cost for classifying it as any possible letter. The learning
vector quantizer (LVQ) was se- lected as neural classifier because, being a
vector.
Abstract Reinforcement learning has proven to be a set of success- ful techniques for finding optimal policies on uncertain and/or dynamic domains, such as the ...
Engineering, Mumbai University. India. +91-9226977842 [email protected]. G J Sharma. Department of Computer Science. K J Somaiya College of ...
In this paper a novel image coding scheme, based on coinbination of fractal and ... processing, such as image segmentation, image analysis, ... DSP 97 - 797 ...
Jul 17, 2007 - stelling), praktische MD-LVQ schemas construeren, die vergelijkbaar met en vaak superieur ..... [69] Alan Jeffrey and Daniel Zwillinger, editors.
Outline. • Vector Quantization: A Brief Introduction. • Vector Quantization:
Properties. • Learning Vector Quantization. • Applications of SOM and LVQ ...
Intro. ANN & Fuzzy Systems
Lecture 38. Learning Vector Quantization (LVQ)
Intro. ANN & Fuzzy Systems
Outline • • • •
Vector Quantization: A Brief Introduction Vector Quantization: Properties Learning Vector Quantization Applications of SOM and LVQ
(C) 2001 by Yu Hen Hu
2
Intro. ANN & Fuzzy Systems
VQ Problem Statement Given a set of vectors {v} drawn from a distribution f(v). The goal of vector quantization is to find an encoding scheme, which is a mapping from v to a code word w = c(v) such that the average distortion D =∫ d (v, w) f (v)dv is minimized, where d(v,w) is a distortion measure chosen appropriately according to specific applications.
(C) 2001 by Yu Hen Hu
3
Intro. ANN & Fuzzy Systems
Vector Quantization = Clustering • Given a set of vectors {x}, find a set of representative vectors {wm; 1 ≤ m ≤ M} such that each x is quantized into a particular wm.
x x• xx x w1
x• x
xx• x
w2
w3
1-D (scaler) quantization
(C) 2001 by Yu Hen Hu
x x x• x x w1
x
x x x x• x w2 x
x x x• x w3
2-D vector quantization
4
Intro. ANN & Fuzzy Systems
Vector Quantization • VQ is data dependent. {wm} locate at the mean (centroid) of the density distribution of each cluster.
x x• xx x w1
(C) 2001 by Yu Hen Hu
x• x w2
xx• x w3
5
Intro. ANN & Fuzzy Systems
VQ and SOM • Relation between VQ and SOM: SOM is a special VQ method with a constraint on spatial ordering. • Relation between VQ and pattern classification: VQ is an unsupervised pattern classifier where the actual class membership information is not used. o x x *x x x x
x
o o *o o
o x x x x x X* x
Closest distance ⇒ correct classification.
SOM (C) 2001 by Yu Hen Hu
6
Intro. ANN & Fuzzy Systems
Learning Vector Quantization (LVQ) • Fine tune SOM result to perform supervised pattern classification by fine tuning the decision boundary. • LVQ1: First, perform SOM. Then, assign each code word to a particular class (class # < codebook size). Correct mis-classification by pushing code word away from current data vector: wm*(t+1) = wm*(t) + η(t) (x –wm*(t)) if x and wm*(t) in the same class. wm*(t+1) = wm*(t) – η(t) (x –wm*(t)) if x and wm*(t) in different classes. wm (t+1) = wm (t) if m ≠ m*.
o error o o* o x o x x *x x o erro x x x x x x x X* x LVQ1
(C) 2001 by Yu Hen Hu
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Intro. ANN & Fuzzy Systems
Learning Vector Quantization (LVQ2) • LVQ2 – Update nearest code word and the second nearest (runner-up) code word with different classes. Denote the indices of them to be i and j:
wi(t+1) = wi(t) + η(t) (x –wi(t)) if x and wi(t) in the same class and x in a window. wj(t+1) = wj(t) – η(t) (x –wj(t)) if x and wj(t) in different classes and in a window. wm(t+1) = wm(t) Otherwise. (C) 2001 by Yu Hen Hu
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Intro. ANN & Fuzzy Systems
LVQ2 Continued • The window is a neighborhood near the decision boundary o o• o o
o x x x xx x• x
o
pull if same class push if diff. class
x x x• x LVQ 2
(C) 2001 by Yu Hen Hu
9
Intro. ANN & Fuzzy Systems
Learning Vector Quantization–3 • LVQ3 – i,j are the indices of the first two nearest codewords. If x and wi(t) are in the same class, x and wj(t) are in different classes, then when x falls within a predefined window, (same as LVQ2) wi(t+1) = wi(t) + η(t) (x –wi(t)) wj(t+1) = wj(t) – η(t) (x –wj(t)) Otherwise, if x, wi(t), and wj(t) are in the same class, (different from LVQ2) wk(t+1) = wk(t) + ε η(t) (x –wk(t)) k = i, j, 0.1 < ε < 0.5 (C) 2001 by Yu Hen Hu
