MACHINE LEARNING TECHNIQUES - LASA

10
Learning and biases
In order to speed up learning, one can introduce biases. These can take two forms: One can
either introduce a bias in the starting conditions, e.g. making sure that you start nearby the global
optima. Or, one can limit the search to a subpart of the search space that contains optima.
1.2.1 Taxonomy of Learning Algorithms
Machine learning algorithms are often organized according to taxonomy, based on the desired
outcome of the algorithm. Common algorithm types include:
• Supervised learning – where the algorithm learns a function or model that maps best a
set of inputs to a set of desired outputs.
• Reinforcement learning – where the algorithm learns a policy or model of the set of
transitions across a discrete set of input-output states (Markovian world) in order to
maximize a reward value (external reinforcement).
• Unsupervised learning – where the algorithm learns a model that best represents a set
of inputs without any feedback (no desired output, no external reinforcement)
• Learning to learn – where the algorithm learns its own inductive bias based on previous
experiences
In all types of learning, it is important that the training examples contain enough information to
enable the system to find the solution, and that they are representative of the complexity of the
whole dataset in order to avoid an overgeneralization.
Machine learning algorithms can also be classified according to the type of computation they can
perform on a given dataset. Common types of computation include:
• Classification: learn to put instances into pre-defined classes
• Association: learn relationships between the attributes
• Clustering: discover classes of instances that belong together
• Numeric prediction: learn to predict a numeric quantity instead of a class
1.2.2 Other important terms in machine learning
On-line learning: an algorithm is said to work on-line or in real-time, when it can perform its
computation on the data as they stream through. Typical example is an algorithm for vision that
could be performed without slowing down the 30 frames per second flow of images.
Connectionist models, more commonly called Artificial Neural Networks (ANN), form a large
part of the algorithms of machine learning. ANNs encompass a diverse set of algorithms that
share the same principle of computation, pseudo-parallel computation.
In connectionist models, unsupervised learning is also known as self-organization, a principle by
which the network self-organizes to best represent the data. Examples of self-organized networks
are Kohonen, Hopfield and other Associative Memories. Examples of supervised networks are
feed-forward and recurrent neural networks with Backpropagation.
© A.G.Billard 2004 – Last Update March 2011