Powerpoint presentation

• Example of intelligent system: OCR• k-Nearest Neighbor Classifier• Generative model• Maximum likelihood• Naïve Bayes model• Gaussian model – Input: scanned images, photos, vdo images– Output: text file – Electronic stylus e.g. PDA– Online handwritten recognition – Image enhancement, denoise, deskew, .
– Binarization • Layout analysis and character segmentation• Character recognition• Spell correction • Preprocessing uses image processing tech.
• Layout analysis uses rule-based + some stats.
• Character recognition – Classifier (trained from training corpus)– Look-up table: no class -> ascii or UTF code • Spell correction uses dictionary + some stats – All separated character: Neural Network, SVM– Few touched characters: Class of touched char– Some broken characters (อำำ): Class of sub-char– Rule-based segmentation – Several touched chars (e.g. arab, urdu): 2D- • Normalize character image (reduce variation, get • Contain more information• Cannot reliably detected – Low-level features: pixels color, edge • Single feature is not meaningful• Can be easily detected• Can be improved: PCA, LDA, NLDA, .
• Design class• Build a feature extractor, ex: vector of pixels color• Construct a training corpus – 1 example = 1 vector and 1 class– Very large number of examples– Cover all conditions: dpi, fonts, font sizes, style (e.g. slant, bold), writing styles, pen styles, • Collect sample• Segment from forms or manual segmentation • Print different fonts, font sizes, .
• Scan, scan of copy, .
• SNNS or fann for Neural Network• libsvm or svmlight for SVM• weka – Format of training corpus– Parameters and their values– How to use it in your code • Biological inspired multi-class classifier• Set of nodes with oriented connections • Try MLP with 1 hidden layer first• 1 parameter = number of hidden nodes• Training with Gradient descent• 1 training parameter = learning rates • Linear classifier using kernel trick trained to tradeoff • output is linear combination of input features• y = sign(wTx)• Use multiple linear classifier for multi-class • Replace all dot product with a kernel function• K(x1,x2) = <g(x1); g(x2)> with some unknown – Small C = generalization is more important than error – Large C = error is more important – gamma = inverse of area of influence around – C = trade off parameter between error on training – Rough classification: upper vowel, mid-level – Rough classification: upper vowel, mid-level – Fine classification: กถภ, ปฝฟ, .
– Finer classification • Prototype-based classifier, template-based classifier• Distance function• Useful when – We have very limited number of training examples, – We have large number of training examples, just to – When n→ ∞, 1NN error < 2 bayes error If we do know P(Class |x),.,P(Class |x), then the Bayes produces the minimum possible expected error = Bayes error N number of examples of class y in training set • Put the input object into the same class as most of – Compute distance between input and each training – Sort in increasing order– Count number of instances from each class amongst • There is no k which is always optimal • Norm-p distance ∣x−y∣p=∑xi−yip1/p distx , y=x−y T −1 x− y dist x , y=K x , xK  y , y −2K x , y  • Solving classification problem = build P(class|input)• P(class |input) = P(input|class ) P(class ) / P(input) • P(input) = Σ P(input|class ) P(class ) • P(class ) = percentage of examples from class i in the • Solving classification problem = build P(input|class )i• P(input|class ) = likelihood of class • P(class |input) = posterior probability of class • To build P(input|class ) we usually made an – How the data from the class i is distributed, e.g. – How each input i.e. document is represented?– What is the likelihood model for these data? • Spam/not-spam• Document = set of words• Preprocessing – word segmentation– remove stop-words– stemming– word selection • Naïve Bayes assumption: all words are • Same hypothesis for all classes• How to compute P(w |Spam), why? • What is the process of building Naïve Bayes • x ,.,x are i.i.d. according to P(x|θ) where θ is the • Q: What is the proper value for θ?• A: The value which gives maximum P(x ,.,x |θ) • Q: We know P(x|θ), how to compute P(x ,.,x |θ)? • Q: How to find the maximum value?• Q: How to get ride of the product? – Q: What this means? What is P(w|Spam)– word “viagra” – {T, F, F, T, T, T, F, T, F, T }– Find proper parameter for P(w|Spam) – {H, T, H, H, T, H, H, H, T, H}– Q: What is the parameter of Binomial • Sometimes, we have prior knowledge about the model, • We search for maximum P(θ|x ,.,x ) instead • Q: How to compute P(θ|x ,.,x ) from P(θ) and P(x|θ)? • Exercise: coin-toss problem θ is distributed as Gaussian with mean 5/10 and standard deviation 1/10 – Q: What is Gaussian model?– Q: What is the proper value for θ? • ML is good when we have large enough data• MAP is prefered when we have small data• Prior can be estimated from data too

Source: http://www.cs.ait.ac.th/vgl/ml/slides/ml2.pdf

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