Data Mining Practical Machine Learning Tools and Techniques

How to Do Things With Words

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1551-6709.2010.01106.x

Composition in distributional models of semantics.

https://dspace.stir.ac.uk/bitstream/1893/25490/1/affective-computing-review.pdf

A review of affective computing

The major change in the second edition of this book is the addition of a new chapter on probabilistic retrieval. This chapter has been included because I think this is one of the most interesting and active areas of research in information retrieval. There are still many problems to be solved so I hope that this particular chapter will be of some help to those who want to advance the state of knowledge in this area. All the other chapters have been updated by including some of the more recent work on the topics covered. In preparing this new edition I have benefited from discussions with Bruce Croft, The material of this book is aimed at advanced undergraduate information (or computer) science students, postgraduate library science students, and research workers in the field of IR. Some of the chapters, particularly Chapter 6 * , make simple use of a little advanced mathematics. However, the necessary mathematical tools can be easily mastered from numerous mathematical texts that now exist and, in any case, references have been given where the mathematics occur. I had to face the problem of balancing clarity of exposition with density of references. I was tempted to give large numbers of references but was afraid they would have destroyed the continuity of the text. I have tried to steer a middle course and not compete with the Annual Review of Information Science and Technology. Normally one is encouraged to cite only works that have been published in some readily accessible form, such as a book or periodical. Unfortunately, much of the interesting work in IR is contained in technical reports and Ph.D. theses. For example, most the work done on the SMART system at Cornell is available only in reports. Luckily many of these are now available through the National Technical Information Service (U.S.) and University Microfilms (U.K.). I have not avoided using these sources although if the same material is accessible more readily in some other form I have given it preference. I should like to acknowledge my considerable debt to many people and institutions that have helped me. Let me say first that they are responsible for many of the ideas in this book but that only I wish to be held responsible. My greatest debt is to Karen Sparck Jones who taught me to research information retrieval as an experimental science. Nick Jardine and Robin …

Information retrieval

Fuzzy regression trees are a generalization of the standard artificial intelligence technique of regression trees. The Elgasir algorithm has previously been used to create fuzzy regression trees in order to improve the performance of crisp regression trees. A weakness of this approach was that no optimisation of tree node membership functions took place. Artificial Immune Systems are an evolutionary methodology, inspired by the principles and processes of the natural immune system. In this paper a novel method based on an optimization version of Artificial Immune Network model (opt-aiNet) is used to optimize the Elgasir algorithm. In order to illustrate the prediction accuracy of the proposed method, two problem datasets from the UCI repository are used to evaluate the approach. Experimental results have shown the effectiveness of using opt-aiNet for optimization Elgasir algorithm by increasing the prediction accuracy and robustness of fuzzy regression trees.

Immune engineering for Elgasir algorithm optimization

There is disclosed a method for analyzing the behavior of a subject comprising the steps of: making one or more measurements or observations of the subject; coding the measurements or observations into a plurality of channels; and analyzing the channels using artificial intelligence, in order to output information relating to the psychology of the subject.

Methods and apparatus for analysing the behaviour of a subject

This paper presents a novel Genetic Algorithm (GA) [1], [2] based approach for decision tree induction. Decision tree induction algorithms such as ID3 [3], [4] and CHAID [5], are based on a stepwise search procedure. This is essentially a heuristic search technique based on selecting the best local attribute/values split for each internal node. This is performed according to some given criteria, regardless of the impact on subsequent splits. Once a split is selected, these algorithms have no backtracking mechanism to enable them to change an attribute split. Thus a potential weakness of these algorithms is the lack of a globally optimal search strategy.

Genetic Algorithms For Decision Tree Induction

This paper presents a new approach to combining multiple fuzzy regression trees, which are induced by applying the modified Elgasir fuzzy regression tree algorithm. This method utilises Trapezoidal membership functions for fuzzification and the Takagi-Sugeno fuzzy inference to obtain the final predicted values. A modified version of Artificial Immune Network model (opt-aiNet) is used for the simultaneous optimization of the membership functions across all trees within the forest. Boston housing and Abalone are two real-world datasets from the UCI repository used to evaluate the proposed approach. The empirical results have showed that fuzzy regression tree forests reduce the error rate compared with single fuzzy regression tree.

An architecture for constructing fuzzy regression tree forests using opt-aiNet

Natural language dialogue is an important component of interaction between ordinary users and complex computer applications. Short Text Semantic Similarity algorithms have been developed to improve the efficiency of producing sophisticated dialogue systems. Such algorithms are currently unable to discriminate between different dialogue acts (assertions, questions, instructions etc.), requiring the addition of efficient dialogue act classifiers to enhance them. The Slim Function Word Classifier (SFWC) has proved promising, particularly in its computational simplicity. This study optimizes the SFWC by clustering function word features using grammatical principles. Experiments show a significant improvement in classification accuracy for a selection of sentence forms which were challenging for the unoptimized SFWC. Results are expected to be applicable to many intelligent text processing applications ranging from question answering to meeting summarization.

Zuhair Bandar

Papers

Immune engineering for Elgasir algorithm optimization

Methods and apparatus for analysing the behaviour of a subject

Genetic Algorithms For Decision Tree Induction

An architecture for constructing fuzzy regression tree forests using opt-aiNet

Optimizing Features for Dialogue Act Classification