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JugMarche: Machine learning: usi pratici di supervised learning

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JugMarche: Machine learning: usi pratici di supervised learning

  1. 1. Onofrio Panzarinohttp://onof80.blogspot.com JUG Marche 29 ottobre 2011
  2. 2. Perché Possiamo dare un importante valore aggiunto per l’utente Possiamo risparmiarci alcuni rompicapi implementativi  Approcci più generici
  3. 3. Interazione utente
  4. 4. Supporto alle decisioni Social network  Forse vorresti iscriverti a questo gruppo… E-commerce  Forse ti interessa anche questo articolo….  Forse vuoi visitare il sito del nostro partner… CMS  Forse vuoi usare anche questo tag…  Forse vuoi linkare questa risorsa… Enterprise App  Hai utilizzato questa regola: …, forse vuoi impostarla come default.
  5. 5. Machine learning Machine learning  Learning: «Generalizzare dall’esperienza» Generalizzare → Classificare  Molti dei problemi si riducono a problemi di classificazione  Predizione ○ di un comportamento ○ di un dato ○ di un evento
  6. 6. Supervised learning Training set  un set di dati di ingresso di cui conosciamo già l’uscita Il programma «apprende» ed è in grado di dare una risposta per nuovi dati d’ingresso L’errore dell’algoritmo si calcola ridando i dati di training in ingresso all’algoritmo
  7. 7. k-nearest neighbor Quando  Esiste (o è definibile) un concetto quantificabile di distanza tra gli oggetti  Abbiamo poca memoria ma una discreta potenza di calcolo La classe di un oggetto è quella più frequente tra i k oggetti del training set più vicini ad esso.
  8. 8. k-NN All’aumentare di k siamo meno sensibili al «rumore» Ma siamo più sensibili allo «scattering»
  9. 9. k-NN: Pros & Cons Pro  Semplicissimo da implementare Cons  Può essere molto lento  In realtà non c’è apprendimento ○ Le informazioni rimangono nel training set che dobbiamo portarci dietro ovunque ○ È come se per riconoscere un animale dovessimo confrontarlo con tutti quelli che conosciamo!
  10. 10. Bayes Classifier
  11. 11. Bayes: Pros & Cons Pros  Semplice da implementare  Funziona anche con pochi dati  Niente tuning. Forse. Cons  I dati devono essere dotati di caratteristiche il più possibile indipendenti  Sensibile al trattamento iniziale dei dati (importante)
  12. 12. Decision Tree A volte non è facile prevedere il modo di ragionare dell’utente  Invece di pensarli tutti, un programma potrebbe estrapolarlo dalle scelte pregresse Alberi di decisione  Permettono di stabilire anche il «come» si è arrivati ad una certa classificazione  Sono l’obbiettivo del training  Ma portano anche molta informazione facilmente fruibile
  13. 13. ID3
  14. 14. Logistic regression Il gradino è un algoritmo di classificazione Ingressi:  Molti possibili valori Uscita  Numero finito di classi
  15. 15. Logistic regression:sigmoide
  16. 16. Logistic regression: training Dobbiamo trovare un «buon» valore per w. Uno potrebbe essere quello che minimizza l’errore Discesa lungo il gradiente  Si parte da un valore iniziale di w  Per ogni elemento nel training set ○ Si calcola l’errore usando la funzione logistica ○ Si modifica opportunamente w in modo da andare nella «direzione» che minimizza l’errore  Si ripete questo ultimo passo «un po’» di volte.
  17. 17. LR: ottimizzazioni Stochastic Gradient Ascent / Descent  Invece che ricalcolare per ogni elemento nel training set, si calcola in modo su n elementi applicando una correzione via via maggiore  Si guadagna in velocità a scapito dell’accuratezza
  18. 18. LR: Pros & Cons PROS  Una volta addestrato: ○ Poca memoria ○ Funzione di classificazione semplice e rapida  Di facile implementazione CONS  I dati devono essere convertibili in floating-point  Solo due classi (sì / no)  Richiede tuning
  19. 19. Support Vector Machine Abbiamo l’iperspazio dei nostri oggetti da classificare Le classi possono essere viste come delle regioni che li racchiudono  Posso rappresentarle con dei vettori  Con questi vettori posso classificare nuovi elementi per capire in quale parte dello spazio si trovano
  20. 20. SVM: Ottimizzazioni Trovare questi vettori può essere molto gravoso  In passato richiedeva grande potenza di calcolo Sequential Minimal Optimization (Platt)  Riduce il numero di iterazioni necessarie per addestrare una SVM Problemi non lineari?  Analisi e rimappatura su iperspazi a più dimensioni ○ Kernel
  21. 21. SVM: Pros & Cons Pros  Robusto  Efficiente Cons  Difficile implementazione  Sensibile a parametri di tuning ed eventuale rimappatura
  22. 22. Adaboost Fare il tuning di un classificatore richiede tempo ed energie Prendiamo una batteria di classificatori  Scegliamo come risultato una media pesata dei risultati  Ci servono ○ Pesi ○ Classificatori addestrati  Come?
  23. 23. Adaboost: training Consideriamo una famiglia di classificatori  e una funzione che ci permette di trovare il classificatore che minimizza l’errore su una distribuzione D relativa ad un training set. Inizializziamo D Ripeti N volte  Prendi il miglior classificatore considerando D  L’errore medio sarà il «peso» del classificatore  Aggiorniamo D
  24. 24. Ora alcune demo Bayes  Classificazione di documenti per argomento Logistic regression, Bayes, k-nn  Riconoscimento di gesture ID3  Creazione di un decision tree per un sito di annunci di lavoro Progetto scala-recog  http://code.google.com/p/scala-recog/
  25. 25. Riferimenti Peter Harrington, Machine Learning in Action, Manning 2011 Hosmer, David W.; Stanley Lemeshow (2000). Applied Logistic Regression, Wiley. ISBN 0-471-35632-8. Platt, John (1998), Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines

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