Tags » SVM

Gabor Wavelet Based Features Extraction for RGB Objects Recognition

Gabor features, a well-researched topic, widely used in image processing applications such as object and faces recognition, also pattern recognition applications such as fingerprint recognition, character recognition, and texture segmentation etc. 950 more words


Review for Belong to You by RandomDarkness

I had read this one before I got sick, and it has been sitting on my iPad all this time for me to come back and read it. 662 more words

Story Review

Review for Bored to Death by EricIzMine

This one is hard for me to do, but at the same time, it is long overdue.

For a very long time, if you thought of SVM FanFiction, EricIzMine’s Multiverse is one of the staples of the fandom.   895 more words

Story Review

Throw Back Thursday

Welcome to this week’s version of Throwback Thursday.

I give you I Shot the Sheriff by Thyra10.


Customer Churn Prediction with SVM using Scikit-Learn

Support Vector Machine (SVM) is unique among the supervised machine learning algorithms in the sense that it focuses on training data points along the separating hyper planes. 2,261 more words

Predictive Analytic

Artificial Intelligence - Helicopter view of underlying structures

The document presenting an high level view of the underling structures of Artificial Intelligence, Machine Learning and Data Mining

Read the article: AI

Artificial Intelligence

Python Prototype of Grid Search for SVM Parameters

from itertools import product
from pandas import read_table, DataFrame
from sklearn.cross_validation import KFold as kfold
from sklearn.svm import SVC as svc
from sklearn.metrics import roc_auc_score as auc

df = read_table('credit_count.txt', sep = ',')

c = 
g = 
parms = 
kf = 
final = DataFrame()

for i in parms:
  result = DataFrame()	
  mdl = svc(C = i[0], gamma = i[1], probability = True, random_state = 0)
  for j in kf:
    X1 = X.iloc]
    Y1 = Y.iloc]
    X2 = X.iloc]
    Y2 = Y.iloc]
    mdl.fit(X1, Y1)
    pred = mdl.predict_proba(X2)[:, 1]
    out = DataFrame({'pred': pred, 'y': Y2})
    result = result.append(out)
  perf = DataFrame({'Cost': i[0], 'Gamma': i[1], 'AUC': }) 
  final = final.append(perf) 33 more words
Statistical Models