Machine learning and Neural Networks

Machine Learning and Neural Networks Riccardo Rizzo Italian National Research Council Institute for Educational and Training Technologies Palermo - Italy

Definitions Machine learning investigates the mechanisms by which knowledge is acquired through experience Machine Learning is the field that concentrates on induction algorithms and on other algorithms that can be said to ``learn.''

Model A model of learning is fundamental in any machine learning application: who is learning (a computer program) what is learned (a domain) from what the learner is learning (the information source)

A domain Concept learning is one of the most studied domain: the learner will try to come up with a rule useful to separate positive examples from negative examples.

The information source examples: the learner is given positive and negative examples queries: the learner gets information about the domain by asking questions experimentation: the learner may get information by actively experiment with the domain

Other component of the model are the prior knowledge of the learner about the domain. For example the learner may know that the unknown concept can be represented in a certain way the performance criteria that defines how we know that the learner has learned something and how it can demonstrate it. Performance criteria can include: off line or on line measures descriptive or predictive output accuracy efficiency

What techniques we will see kNN algorithm Winnow algorithm Naïve Bayes classifier Decision trees Reinforcement learning (Rocchio algorithm) Genetic algorithm

k-NN algorithm The definition of k-nearest neighbors is trivial: Suppose that each esperience can be represented as a point in an space For a particular point in question, find the k points in the population that are nearest to the point in question. The class of the majority of the of these neighbors is the class to the selected point.

k-NN algorithm c 2 c c1 c4 c3 c4 c1 c2 c2 c3 c4 1 New input Inputs already classified Class 1

k-NN algorithm Finding the k-nearest neighbors reliably and efficiently can be difficult. Other metrics that the Euclidean can be used. The implicit assumption in using any k-nearest neighbors technique is that items with similar attributes tend to cluster together.

k-NN algorithm The k-nearest neighbors method is most frequently used to tentatively classify points when firm class bounds are not established. The learning is done using only positive examples not negative.

k-NN algorithm Used in Schwab, I., Pohl, W., and Koychev, I. (2000) Learning to recommend from positive evidence. In: H. Lieberman (ed.) Proceedings of 2000 International Conference on Intelligent User Interfaces, New Orleans, LA, January 9-12, 2000, ACM Press, pp. 241-247

Winnow Algorithm Is useful to distinguish binary patterns into two classes using a threshold S and a set of weights the pattern x holds to the class y=1 if (1)

Winnow Algorithm The algorithm: take an example ( x , y) generate the answer of the classifier if the answer is correct do nothing else apply some correction

Winnow Algorithm If y’>y the the weights are too high and are diminished If y’<y the the weights are too low and are corrected in both cases are corrected only the ones corresponding to

Winnow Algorithm application Used in M.J. Pazzani “ A framework for Collaborative, Content Based and Demographic Filtering” Artificial Intelligence Review, Dec 1999 R.Armstrong, D. Freitag, T. Joachims, and T. Mitchell " WebWatcher : A Learning Apprentice for the World Wide Web " 1995.

Naïve Bayes Classifier Bayes theorem : given an Hypotesis H, an Evidence E and a context c

Naïve Bayes Classifier Suppose to have a set of objects that can hold to two categories, y 1 and y 2 , described using n features x 1 , x 2 , …, x n . If then the object holds to the category y 1 We drop the context

Naïve Bayes Classifier Using the Bayes theorem: Supposing that all the features are not correlated

Naïve Bayes Classifier Used in: Mladenic, D. (2001) Using text learning to help Web browsing. In: M. Smith, G. Salvendy, D. Harris and R. J. Koubek (eds.) Usability evaluation and interface design. Vol. 1, (Proceedings of 9th International Conference on Human-Computer Interaction, HCI International'2001, New Orleans, LA, August 8-10, 2001) Mahwah, NJ: Lawrence Erlbaum Associates, pp. 893-897. Schwab, I., Pohl, W., and Koychev, I. (2000) Learning to recommend from positive evidence. In: H. Lieberman (ed.) Proceedings of 2000 International Conference on Intelligent User Interfaces, New Orleans, LA, January 9-12, 2000, ACM Press, pp. 241-247, also available at .Self, J. (1986) The application of machine learning to student modelling. Instr. Science, Instructional Science 14, 327-338 .

Naïve Bayes Classifier Bueno D., David A. A. (2001) METIORE: A Personalized Information Retrieval System. In M. Bauer, P. J. Gmytrasiewicz and J. Vassileva (eds.) User Modeling 2001. Lecture Notes on Artificial Intelligence, Vol. 2109, (Proceedings of 8th International Conference on User Modeling, UM 2001, Sonthofen, Germany, July 13-17, 2001) Berlin: Springer-Verlag, pp. 188-198. Frasconi P., Soda G., Vullo A., Text Categorization for Multi-page Documents: A HybridNaive Bayes HMM Approach, ACM JCDL’01, June 24-28, 2001

Decision trees A decision tree is a tree whose internal nodes are tests (on input patterns) and whose leaf nodes are categories (of patterns). Each test has mutually exclusive and exhaustive outcomes.

Decision trees T 1 T 3 T 2 T 4 1 2 1 3 2 1 3 classes 4 tests (maybe 4 variables)

Decision trees The test: might be multivariate (tests on several features of the input) or univariate (test only one feature); might have two or more outcomes. The features can be categorical or numerical.

Decision trees Suppose to have n binary features The main problem in learning decision trees is to decide the order of tests on variables In order to decide, the average entropy of each test attribute is calculated and the lower one is chosen.

Decision trees If we have binary patterns and a set of pattern  it is possible to write the entropy as were p(i|  ) is the probability that a random pattern from  belongs to the class i

Decision trees We will approximate the probability p(i|  ) using the number of patterns in  belonging to the class i divided by the total number of pattern in 

Decision trees If a test T have k outcomes, k subsets  1 ,  2 , ...  k , are considered with n 1 , n 2 , …, n k patterns. It is possible to calculate: T 1 ... ... J K

Decision trees The average entropy over all the  j again we evaluate p(  j ) has the number of patterns in  that outcomes j divided by the total number of patterns in 

Decision trees We calculate the average entropy for all the test T and chose the lower one. We write the part of the tree and go head in order to chose again the test that gives the lower entropy

Decision trees The knowledge in the tree is strongly dependent from the examples

Reinforcement Learning An agent tries to optimize its interaction with a dynamic environment using trial and error. The agent can make an action u that applied to the environment changes its state from x to x’ . The agent receives a reinforcement r .

Reinforcement Learning There are three parts of a Reinforcement Learning Problem: The environment The reinforcement function The value function

Reinforcement Learning The environment at least partially observable by means of sensors or symbolic description. The theory is based on an environment that shows its “true” state.

Reinforcement Learning The reinforcement function a mapping from the couple (state, action) to the reinforcement value. There are three classes of reinforcement functions: Pure delayed reward : the reinforcements are all zero except for the terminal state (games, inverted pendulum) Minimum time to goal : cause an agent to perform actions that generate the shortest path to a goal state

Reinforcement Learning Minimization : the reinforcement is a function of of limited resources and the agent have to achieve the goal while minimizing the energy used

Reinforcement Learning The Value Function : defines how to choose a “good” action. First we have to define policy (state) action value of a state I (following a defined policy) the optimal policy maximize the value of a state T is the final state

Reinforcement Learning The Value Function is a mapping (state) State Value If the optimal value function is founded the optimal policy can be extracted.

Reinforcement Learning Given a state x t V * (x t ) is the optimal state value; V(x t ) is the approximation we have; where e(x t ) is the approximation error

Reinforcement Learning Moreover where  is a discount factor that causes immediate reinforcement to have more importance than future reinforcements

Reinforcement Learning We can find that gives (**)

Reinforcement Learning The learning process goal is to find an approximation V(x t ) that makes the equation (**) true for all the state. The finale state T of a process has a value that is defined a priori so e(T)=0 , so e(T-1)=0 it the (**) is true and then backwards to the initial state.

Reinforcement Learning Assuming that the function approximator for the V * is a look-up table (a table with an approximate state value w for each state) then it is possible to sweep through the state space and update the values in the table according to:

Reinforcement Learning where u is the action performed that causes the transition to the state x t+1 . This must be done by using some kind of simulation in order to evaluate

Reinforcement Learning The last equation can be rewritten as Each update reduce the value of e(x t+1 ) the learning stops when e(x t+1 )=0

Rocchio Algorithm Used in Relevance Feedback in IR We represent a user profile and the objects (documents) using the same space m represents the user w represent the objects (documents)

Rocchio Algorithm The object (document) is matched to the user using an available matching criteria (cosine measure) The user model is updated using where s is a function of the feedback

Rocchio Algorithm It is possible to use a collection of vectors m to represent the user’s interests

Rocchio and Reiforcement Learning The goal is to have the “best” user’s profile The state is defined by the weight vector of the user profile

Rocchio Algorithm (IR) where Q is the vector of the initial query R i is the vector for relevant document S i is the vector for the irrelevant documents  ,  are Rocchio’s weights

Rocchio algorithm Used in Seo, Y.-W. and Zhang, B.-T. (2000) A reinforcement learning agent for personalized information filtering. In: H. Lieberman (ed.) Proceedings of 2000 International Conference on Intelligent User Interfaces, New Orleans, LA, January 9-12, 2000, ACM Press, pp. 248-251 Balabanovic M. “ An Adaptive Web Page Recomandation Service in Proc. Of 1 th International Conference on Autonomous Agents 1997

Genetic Algorithms Genetic algorithms are inspired by natural evolution. In the natural world, organisms that are poorly suited for an environment die off, while those well-suited for it prosper. Each individual is a bit-string that encodes its characteristics. Each element of the string is called a gene .

Genetic Algorithms Genetic algorithms search the space of individuals for good candidates. The "goodness" of an individual is measured by some fitness function . Search takes place in parallel, with many individuals in each generation.

Genetic Algorithms The algorithm consists of looping through generations. In each generation, a subset of the population is selected to reproduce; usually this is a random selection in which the probability of choice is proportional to fitness.

Genetic Algorithms Reproduction occurs by randomly pairing all of the individuals in the selection pool, and then generating two new individuals by performing crossover , in which the initial n bits (where n is random) of the parents are exchanged. There is a small chance that one of the genes in the resulting individuals will mutate to a new value.

Neural Networks An artificial network consists of a pool of simple processing units which communicate by sending signals to each other over a large number of weighted connections.

Artificial Neuron x 1 x 2 x n w 1j w 2j w nj y j b j

Neural Networks Each unit performs a relatively simple job: receive input from neighbors or external sources and use this to compute an output signal which is propagated to other units ( Test stage ). Apart from this processing, there is the task of the adjustment of the weights ( Learning stage ). The system is inherently parallel in the sense that many units can carry out their computations at the same time.

Neural Networks 1. Learning stage 2. Test stage (working stage) Your knowledge is useless !!

Classification (connections) As for this pattern of connections, the main distinction we can make is between: Feed-forward networks , where the data flow from input to output units is strictly feed-forward. The data processing can extend over multiple layers of units, but no feedback connections or connections between units of the same layer are present.

Classification Recurrent networks that do contain feedback connections. Contrary to feed-forward networks, the dynamical properties of the network are important. In some cases, the activation values of the units undergo a relaxation process such that the network will evolve to a stable state in which these activations do not change anymore. Classification (connections)

Recurrent Networks In other applications, the change of the activation values of the output neurons are significant, such that the dynamical behavior constitutes the output of the network.

Classification (Learning) We can categorise the learning situations in two distinct sorts. These are: Supervised learning in which the network is trained by providing it with input and matching output patterns. These input-output pairs are usually provided by an external teacher.

Unsupervised learning in which an (output) unit is trained to respond to clusters of pattern within the input. In this paradigm the system is supposed to discover statistically salient features of the input population. Unlike the supervised learning paradigm, there is no a priori set of categories into which the patterns are to be classified; rather the system must develop its own representation of the input stimuli. Classification (Learning)

Perceptron A single layer feed-forward network consists of one or more output neurons, each of which is connected with a weighting factor w ij to all of the inputs x i . x i b b

Perceptron In the simplest case the network has only two inputs and a single output. The output of the neuron is: suppose that the activation function is a threshold

Perceptron In this example the simple network (the neuron) can be used to separate the inputs in two classes. The separation between the two classes is given by

Perceptron x 1 x 2 x x x x x x x x x

Learning in Perceptrons The weights of the neural networks are modified during the learning phase

Learning in Perceptrons Start with random weights Select an input couple ( x , d( x )) if then modify the weight according with Note that the weights are not modified if the network gives the correct answer

Convergence theorem If there exists a set of connection weights w * which is able to perform the transformation y = d(x), the perceptron learning rule will converge to some solution (which may or may not be the same as w * ) in a finite number of steps for any initial choice of the weights.

Linear Units x 2 x n w 1j w 2j w nj b j Y j =s j

The Delta Rule 1 The idea is to make the change of the weight proportional to the negative derivative of the error

Backpropagation The multi-layer networks with a linear activation can classify only linear separable inputs or, in case of function approximation, only linear functions can be represented.

Backpropagation . . . x 1 x 2 x n v jk h j w ij y i

Backpropagation When a learning pattern is clamped, the activation values are propagated to the output units, and the actual network output is compared with the desired output values, we usually end up with an error in each of the output units. Let's call this error e o for a particular output unit o . We have to bring e o to zero.

Backpropagation The simplest method to do this is the greedy method: we strive to change the connections in the neural network in such a way that, next time around, the error e o will be zero for this particular pattern. We know from the delta rule that, in order to reduce an error, we have to adapt its incoming weights according to the last equation (1)

Backpropagation In order to adapt the weights from input to hidden units, we again want to apply the delta rule. In this case, however, we do not have a value for for the hidden units.

Backpropagation Calculate the activation of the hidden units

Backpropagation And the activation of the output units

Backpropagation If we have  pattern to learn the error is

Backpropagation The weight correction is given by : Where If m is the output layer If m is an hidden layer or

Recurrent Networks What happens when we introduce a cycle? For instance, we can connect a hidden unit with itself over a weighted connection, connect hidden units to input units, or even connect all units with each other ?

Hopfield Network The Hopfield network consists of a set of N interconnected neurons which update their activation values asynchronously and independently of other neurons. All neurons are both input and output neurons. The activation values are binary (+1, -1)

Hopfield Network The state of the system is given by the activation values y = (y k ). The net input s k (t +1) of a neuron k at cycle (t +1) is a weighted sum

Hopfield Network A threshold function is applied to obtain the output

Hopfield Network A neuron k in the net is stable at time t I.e. A state is state if all the neurons are stable

Hopfield Networks If w jk = w kj the behavior of the system can be described with an energy function This kind of network has stable limit points

Hopfield net. applications A primary application of the Hopfield network is an associative memory. The states of the system corresponding with the patterns which are to be stored in the network are stable. These states can be seen as `dips' in energy space.

Hopfield Networks It appears, however, that the network gets saturated very quickly, and that about 0.15N memories can be stored before recall errors become severe.

Hopfield Networks Stable state State state Input

Hopfield Networks Used in Chung, Y.-M., Pottenger, W. M., and Schatz, B. R. (1998) Automatic subject indexing using an associative neural network. In: I. Witten, R. Akscyn and F. M. Shipman III (eds.) Proceedings of The Third ACM Conference on Digital Libraries (Digital Libraries '98), Pittsburgh, USA, June 23-26, 1998, ACM Press, pp. 59-6

Self Organization The unsupervised weight adapting algorithms are usually based on some form of global competition between the neurons. Applications of self-organizing networks are:

S.O. Applications clustering: the input data may be grouped in `clusters' and the data processing system has to find these inherent clusters in the input data.

S.O. Applications vector quantisation : this problem occurs when a continuous space has to be discretised. The input of the system is the n-dimensional vector x , the output is a discrete representation of the input space. The system has to find optimal discretisation of the input space.

S.O. Applications dimensionality reduction : the input data are grouped in a subspace which has lower dimensionality than the dimensionality of the data. The system has to learn an “optimal” mapping.

S.O. Applications feature extraction: the system has to extract features from the input signal. This often means a dimensionality reduction as described above.

Self-Organizing Networks Learning Vector Quantization Kohonen maps Principal Components Networks Adaptive Resonance Theory

Kohonen Maps In the Kohonen network, the output units are ordered in some fashion, often in a two-dimensional grid or array, although this is application-dependent.

Kohonen Maps The input x is given to all the units at the same time

Kohonen Maps The weights of the winner unit are updated together with the weights of its neighborhoods

Kohonen Maps Used in: Fulantelli, G., Rizzo, R., Arrigo, M., and Corrao, R. (2000) An adaptive open hypermedia system on the Web. In: P. Brusilovsky, O. Stock and C. Strapparava (eds.) Adaptive Hypermedia and Adaptive Web-Based Systems. Lecture Notes in Computer Science, (Proceedings of Adaptive Hypermedia and Adaptive Web-based Systems, AH2000, Trento, Italy, August 28-30, 2000) Berlin: Springer-Verlag, pp. 189-201. Goren-Bar, D., Kuflik, T., Lev, D., and Shoval, P. (2001) Automating personal categorizations using artificial neural network. In: M. Bauer, P. J. Gmytrasiewicz and J. Vassileva (eds.) User Modeling 2001. Lecture Notes on Artificial Intelligence, Vol. 2109, (Proceedings of 8th International Conference on User Modeling, UM 2001, Sonthofen, Germany, July 13-17, 2001) Berlin: Springer-Verlag, pp. 188-198.

Kohonen Maps Kayama, M. and Okamoto, T. (1999) Hy-SOM: The semantic map framework applied on an example case of navigation. In: G. Gumming, T. Okamoto and L. Gomez (eds.) Advanced Research in Computers and Communications in Education. Frontiers ub Artificial Intelligence and Applications, Vol. 2, (Proceedings of ICCE'99, 7th International Conference on Computers in Education, Chiba, Japan, 4-7 November, 1999) Amsterdam: IOS Press, pp. 252-259. Taskaya, T., Contreras, P., Feng, T., and Murtagh, F. (2001) Interactive visual user interfaces to databases. In: M. Smith, G. Salvendy, D. Harris and R. J. Koubek (eds.) Usability evaluation and interface design. Vol. 1, (Proceedings of 9th International Conference on Human-Computer Interaction, HCI International'2001, New Orleans, LA, August 8-10, 2001) Mahwah, NJ: Lawrence Erlbaum Associates, pp. 913-917.

Papers on Self--Organizing Networks used in Information organization Honkela, T., Kaski S., Lagus K., and Kohonen T., Newsgroup exploration with WEBSOM method and browsing interface, Technical Report A32, Helsinki University of Technology, Laboratory of Computer and Information Science, Espoo. WEBSOM home page (1996) available at http:// websom .hut.fi/ websom / . Kaski S., Honkela T., Lagus K., Kohonen T., Creating an order in digital libraries with self-organizing maps , in Proc. of WCNN'96, World Congress on Neural Networks, (San Diego, Sept. 15-18, 1996), pp. 814-817. Kaski S., Data exploration using self-organizing maps. Acta Polytecnica Scandinavica, Mathematics, Computing and Management in Engineering Series No. 82, Espoo 1997, 57 pp. Published by the Finnish Academy of Technology. Kohonen T., Kaski S., Lagus K., Honkela T., Very Large Two-Level SOM for the Browsing of the Newsgroups, in Proc. of ICANN'96, (Bochum, Germany, July 16-19 1996), Lecture Notes in Computer Science, Spriger, vol.112, pp 269-274.

Papers on Self--Organizing Networks used in Information organization 2 Lagus K., Honkela T., Kaski S., Kohonen T., WEBSOM--Self Organizing maps of Document Collections , Neurocomputing 21 (1998), 101-117 Lin X., Soergel D., Marchionini G., A Self-Organizing Semantic Map for Information Retrieval, in Proc. of the Fourteenth Annual International ACM/SIGIR Conference on Research and Development in Information Retrieval (Chicago IL, Oct. 13-16, 1991), pp. 262-269. Merkel D., Rauber A., Self-Organization of Distributed Document Archives , in Proc. of the 3rd Int'l Database Engineering and Applications Symposium, IDEAS'99, (Montreal, Canada, Aug. 2-4, 1999). Rauber A., Merkel D., Creating an Order in Distributed Digital Libraries by Integrating Independent Self-Organizing Maps , in Proc. of ICANN'98, (Skovde, Sweden, Sept. 2-4, 1998). Merkel D., Tjoa M., Kappel G., A Self--Organizing Map that Learns the Semantic Similarity of Reusable Software Components , ACNN'94, Jan 31-Feb 2, 1994, pp. 13-16.

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