Resources for learning how to implement ensemble methods I understand theoretically (sort of) how they would work, but am not sure how to go about actually making use an ensemble method (such as voting, weighted mixtures, etc.). 


*

*What are good resources for implementing ensemble methods?

*Are there any particular resources regarding implementation in Python?


EDIT:
To clear up some based on the discussion on the comments, I'm not looking for ensemble algorithms such as randomForest, etc. Instead, I'm wondering how can you combine different classifications from different algorithms.
For example, say someone uses logistic regression, SVM, and some other methods to predict the class of a certain observation. What is the best way to go about capturing the best estimate of the class based upon these predictions? 
 A: 'Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions', Seni and Elder --
Excellent reference on practical ensemble theory and implementation, but accompanying code is R based.
'Machine Learning: An Algorithmic Perspective,' S. Marsland --
Excellent Python based practical text, but not as dedicated to pure ensemble concepts as the first reference.
A: Stumpy Joe Pete's response was perfect, but since you mentioned
about a Python implementation, I wanted to mention the brew project
from Universidade Federal de Pernambuco.
https://github.com/viisar/brew
from brew.base import Ensemble
from brew.base import EnsembleClassifier
from brew.combination import import Combiner

# create your Ensemble
clfs = your_list_of_classifiers # [clf1, clf2]
ens = Ensemble(classifiers = clfs)

# create your Combiner
# the rules can be 'majority_vote', 'max', 'min', 'mean' or 'median'
comb = Combiner(rule='majority_vote')

# now create your ensemble classifier
ensemble_clf = EnsembleClassifier(ensemble=ens, combiner=comb)
ensemble_clf.predict(X)

At this point, they have ensemble generation, combination,
pruning and dynamic selection.
Limitations: classification only; no stacking in current public version;
not much documentation.   
A: A good place to start is to get an overview of ensemble learning. Especially you'll want to look at boosting and bagging. Another method was that used by "The Ensemble" team in the Netflix Prize, is called either "blending" or feature stacking.
Then, just find some libraries that implement those and work from there. A quick googling turned up scikit and orange, both of which should have bagging and boosting (and they're both Python).
If beyond just using ensemble methods, you'd like to learn a bit of the theory, then I think this paper would be a good jumping off point (follow the references for the parts you're interested in).
Cheers.
A: Salford Systems has a software package called Random Forests that implements this for classification and regression tree ensembles.  I don't have any free R packages to offer. I imagine they have a users manual that will explain their implementation.  By analogy you could probably figure out how to do it for other ensemble methods.
A: I found this tutorial which was extremely helpful. It doesn't answer all the pieces but I think it is a great start to the discussion: http://vikparuchuri.com/blog/intro-to-ensemble-learning-in-r/
A: Scikit-learn ensembling guide provides bagging and boosting meta-classifiers and regressors. In addition, mlxtend library provides implementations of stacking meta-classifiers and regressors.
