What is the difference if we use Decision Tree as Base estimator in AdaBoost algorithm ?
Is Random Forest a special case of AdaBoost?
Most certainly not; Random Forest is a case of bagging ensemble algorithm (short for bootstrap aggregating), which is different from boosting - check here for their differences.
What is the difference if we use Decision Tree as Base estimator in AdaBoost algorithm ?
You don't get a Random Forest, but a Gradient Tree Boosting Machine, available in several packages like xgboost (R/Python), gbm (R), scikit-learn (Python) etc.
Check chapter 8 of the excellent (and freely available) book An Introduction to Statistical Learning for more, or The Elements of Statistical Learning (heavy in math & theory, not for the faint-hearted)...
How does Multiclass Decision Forest differ from Random Forest? What factors do they have in common? It appears there is not a clear answer on the web regarding this matter.
Random forests or random decision forests is an extension of the decision forests (ensemble of decision trees) combining bagging and random selection of features to construct a collection of decision trees with controlled variance.
A very good paper from Microsoft research you may consider to look at.
What does "Naive" Bayes mean in Machine learning?
Naive Bayes in machine learning typically refers to a set of supervised learning algorithms that apply the Bayes' theorem. It's essentially a "classifier" that helps you classify things based on a series of independent "naive" assumptions. For example if you wanted to use machine learning to help you identify potential fruit... taking a banana, its curved, yellow, and may be 10 inches long. Each of those properties, 'curved', 'yellow', '10 inches long' are all independent properties that come together to form a 'probability' that a fruit is a banana. With this 'naive' bayes classifier, in the future if there are other kinds of 'fruit' or different images, descriptions of fruit that have similar properties, using machine learning, your naive bayes classifier can classify those future fruits or unknown things as bananas correctly... (or incorrectly) in which you'll probably want to identify more 'naive' features to make your classifier more accurate like for example, there might be a 'blackened tip' or 'have a slight greenish color'.
It is called naïve because the model assumes independence between the features. This is a strong assumption which usually is not correct and that is the reason of the name.
Nevertheless, naïve Bayes is quite efficient and in practice is known for giving good results.
A naive Bayes classifier is an algorithm that uses Bayes' theorem to classify objects. Naive Bayes classifiers assume strong, or naive, independence between attributes of data points. Popular uses of naive Bayes classifiers include spam filters, text analysis and medical diagnosis.
What makes a naive Bayes classifier naive is its assumption that all attributes of a data point under consideration are independent of each other. A classifier sorting fruits into apples and oranges would know that apples are red, round and are a certain size, but would not assume all these things at once. Oranges are round too, after all.
A naive Bayes classifier is not a single algorithm, but a family of machine learning algorithms that make uses of statistical independence. These algorithms are relatively easy to write and run more efficiently than more complex Bayes algorithms.
The most popular application is spam filters. A spam filter looks at email messages for certain key words and puts them in a spam folder if they match.
Despite the name, the more data it gets, the more accurate a naive Bayes classifier becomes, such as from a user flagging email messages in an inbox for spam.
What is the exact difference between a Support Vector Machine classifier and a Support Vector Machine regresssion machine?
The one sentence answer is that SVM classifier performs binary classification and SVM regression performs regression.
While performing very different tasks, they are both characterized by following points.
usage of kernels
absence of local minima
sparseness of the solution
capacity control obtained by acting on the margin
number of support vectors, etc.
For SVM classification the hinge loss is used, for SVM regression the epsilon insensitive loss function is used.
SVM classification is more widely used and in my opinion better understood than SVM regression.
I know SVMs are supposedly 'ANN killers' in that they automatically select representation complexity and find a global optimum (see here for some SVM praising quotes).
But here is where I'm unclear -- do all of these claims of superiority hold for just the case of a 2 class decision problem or do they go further? (I assume they hold for non-linearly separable classes or else no-one would care)
So a sample of some of the cases I'd like to be cleared up:
Are SVMs better than ANNs with many classes?
in an online setting?
What about in a semi-supervised case like reinforcement learning?
Is there a better unsupervised version of SVMs?
I don't expect someone to answer all of these lil' subquestions, but rather to give some general bounds for when SVMs are better than the common ANN equivalents (e.g. FFBP, recurrent BP, Boltzmann machines, SOMs, etc.) in practice, and preferably, in theory as well.
Are SVMs better than ANN with many classes? You are probably referring to the fact that SVMs are in essence, either either one-class or two-class classifiers. Indeed they are and there's no way to modify a SVM algorithm to classify more than two classes.
The fundamental feature of a SVM is the separating maximum-margin hyperplane whose position is determined by maximizing its distance from the support vectors. And yet SVMs are routinely used for multi-class classification, which is accomplished with a processing wrapper around multiple SVM classifiers that work in a "one against many" pattern--i.e., the training data is shown to the first SVM which classifies those instances as "Class I" or "not Class I". The data in the second class, is then shown to a second SVM which classifies this data as "Class II" or "not Class II", and so on. In practice, this works quite well. So as you would expect, the superior resolution of SVMs compared to other classifiers is not limited to two-class data.
As far as i can tell, the studies reported in the literature confirm this, e.g., In the provocatively titled paper Sex with Support Vector Machines substantially better resolution for sex identification (Male/Female) in 12-square pixel images, was reported for SVM compared with that of a group of traditional linear classifiers; SVM also outperformed RBF NN, as well as large ensemble RBF NN). But there seem to be plenty of similar evidence for the superior performance of SVM in multi-class problems: e.g., SVM outperformed NN in protein-fold recognition, and in time-series forecasting.
My impression from reading this literature over the past decade or so, is that the majority of the carefully designed studies--by persons skilled at configuring and using both techniques, and using data sufficiently resistant to classification to provoke some meaningful difference in resolution--report the superior performance of SVM relative to NN. But as your Question suggests, that performance delta seems to be, to a degree, domain specific.
For instance, NN outperformed SVM in a comparative study of author identification from texts in Arabic script; In a study comparing credit rating prediction, there was no discernible difference in resolution by the two classifiers; a similar result was reported in a study of high-energy particle classification.
I have read, from more than one source in the academic literature, that SVM outperforms NN as the size of the training data decreases.
Finally, the extent to which one can generalize from the results of these comparative studies is probably quite limited. For instance, in one study comparing the accuracy of SVM and NN in time series forecasting, the investigators reported that SVM did indeed outperform a conventional (back-propagating over layered nodes) NN but performance of the SVM was about the same as that of an RBF (radial basis function) NN.
[Are SVMs better than ANN] In an Online setting? SVMs are not used in an online setting (i.e., incremental training). The essence of SVMs is the separating hyperplane whose position is determined by a small number of support vectors. So even a single additional data point could in principle significantly influence the position of this hyperplane.
What about in a semi-supervised case like reinforcement learning? Until the OP's comment to this answer, i was not aware of either Neural Networks or SVMs used in this way--but they are.
The most widely used- semi-supervised variant of SVM is named Transductive SVM (TSVM), first mentioned by Vladimir Vapnick (the same guy who discovered/invented conventional SVM). I know almost nothing about this technique other than what's it is called and that is follows the principles of transduction (roughly lateral reasoning--i.e., reasoning from training data to test data). Apparently TSV is a preferred technique in the field of text classification.
Is there a better unsupervised version of SVMs? I don't believe SVMs are suitable for unsupervised learning. Separation is based on the position of the maximum-margin hyperplane determined by support vectors. This could easily be my own limited understanding, but i don't see how that would happen if those support vectors were unlabeled (i.e., if you didn't know before-hand what you were trying to separate). One crucial use case of unsupervised algorithms is when you don't have labeled data or you do and it's badly unbalanced. E.g., online fraud; here you might have in your training data, only a few data points labeled as "fraudulent accounts" (and usually with questionable accuracy) versus the remaining >99% labeled "not fraud." In this scenario, a one-class classifier, a typical configuration for SVMs, is the a good option. In particular, the training data consists of instances labeled "not fraud" and "unk" (or some other label to indicate they are not in the class)--in other words, "inside the decision boundary" and "outside the decision boundary."
I wanted to conclude by mentioning that, 20 years after their "discovery", the SVM is a firmly entrenched member in the ML library. And indeed, the consistently superior resolution compared with other state-of-the-art classifiers is well documented.
Their pedigree is both a function of their superior performance documented in numerous rigorously controlled studies as well as their conceptual elegance. W/r/t the latter point, consider that multi-layer perceptrons (MLP), though they are often excellent classifiers, are driven by a numerical optimization routine, which in practice rarely finds the global minimum; moreover, that solution has no conceptual significance. On the other hand, the numerical optimization at the heart of building an SVM classifier does in fact find the global minimum. What's more that solution is the actual decision boundary.
Still, i think SVM reputation has declined a little during the past few years.
The primary reason i suspect is the NetFlix competition. NetFlix emphasized the resolving power of fundamental techniques of matrix decomposition and even more significantly t*he power of combining classifiers. People combined classifiers long before NetFlix, but more as a contingent technique than as an attribute of classifier design. Moreover, many of the techniques for combining classifiers are extraordinarily simple to understand and also to implement. By contrast, SVMs are not only very difficult to code (in my opinion, by far the most difficult ML algorithm to implement in code) but also difficult to configure and implement as a pre-compiled library--e.g., a kernel must be selected, the results are very sensitive to how the data is re-scaled/normalized, etc.
I loved Doug's answer. I would like to add two comments.
1) Vladimir Vapnick also co-invented the VC dimension which is important in learning theory.
2) I think that SVMs were the best overall classifiers from 2000 to 2009, but after 2009, I am not sure. I think that neural nets have improved very significantly recently due to the work in Deep Learning and Sparse Denoising Auto-Encoders. I thought I saw a number of benchmarks where they outperformed SVMs. See, for example, slide 31 of
http://deeplearningworkshopnips2010.files.wordpress.com/2010/09/nips10-workshop-tutorial-final.pdf
A few of my friends have been using the sparse auto encoder technique. The neural nets build with that technique significantly outperformed the older back propagation neural networks. I will try to post some experimental results at artent.net if I get some time.
I'd expect SVM's to be better when you have good features to start with. IE, your features succinctly capture all the necessary information. You can see if your features are good if instances of the same class "clump together" in the feature space. Then SVM with Euclidian kernel should do the trick. Essentially you can view SVM as a supercharged nearest neighbor classifier, so whenever NN does well, SVM should do even better, by adding automatic quality control over the examples in your set. On the converse -- if it's a dataset where nearest neighbor (in feature space) is expected to do badly, SVM will do badly as well.
- Is there a better unsupervised version of SVMs?
Just answering only this question here. Unsupervised learning can be done by so-called one-class support vector machines. Again, similar to normal SVMs, there is an element that promotes sparsity. In normal SVMs only a few points are considered important, the support vectors. In one-class SVMs again only a few points can be used to either:
"separate" a dataset as far from the origin as possible, or
define a radius as small as possible.
The advantages of normal SVMs carry over to this case. Compared to density estimation only a few points need to be considered. The disadvantages carry over as well.
Are SVMs better than ANNs with many classes?
SVMs have been designated for discrete classification. Before moving to ANNs, try ensemble methods like Random Forest , Gradient Boosting, Gaussian Probability Classification etc
What about in a semi-supervised case like reinforcement learning?
Deep Q learning provides better alternatives.
Is there a better unsupervised version of SVMs?
SVM is not suited for unsupervised learning. You have other alternatives for unsupervised learning : K-Means, Hierarchical clustering, TSNE clustering etc
From ANN perspective, you can try Autoencoder, General adversarial network
Few more useful links:
towardsdatascience
wikipedia