Learning is needed when there is no human expertise existing or when human beings are unable to explain their expertise. In such a situation, one simply collects all the possible previous information, analyse it and then make a rule for future prediction or taking meaningful decision. When we plan to conclude such a work with the help of a computer by providing it ample amount of data and our past experience with tools and techniques, then the whole process becomes machine learning. Hence, machine learning can be defined as programming computers to optimise a performance criterion using example data and past experience. For example, recognition of spoken speech is being done by human beings seemingly without any difficulty, but cannot explain how they do it.Defence Science Journal, 2010, 60(4), pp.345-347, DOI:http://dx.doi.org/10.14429/dsj.60.50

Kant, Shri

English

Defence Science Journal

345Guest EditorialMachine Learning and Pattern RecognitionLearning is needed when there is no human expertiseexisting or when human beings are unable to explain theirexpertise. In such a situation, one simply collects all thepossible previous information, analyse it and then makea rule for future prediction or taking meaningful decision.When we plan to conclude such a work with the help ofa computer by providing it ample amount of data and ourpast experience with tools and techniques, then the wholeprocess becomes machine learning. Hence, machine learningcan be defined as programming computers to optimisea performance criterion using example data and past experience.For example, recognition of spoken speech is being doneby human beings seemingly without any difficulty, butcannot explain how they do it. In machine learning, theapproach is to collect a large sample of utterances frompeople having different age, gender, or accent and learnto map these to words. It means to solve a problem whichchanges with time or depends on the particular environment;we would like to have a general purpose system that canadapt to its circumstances. In robotics, robots may learnto optimise their behaviour to complete a particular missionwith limited resources.Machine learning is an exciting field for many futureapplications like auto-driven cars under different roadand weather conditions, multilingual telephony on real-time basis with translating facility to and from a foreignlanguage, and autonomous robots that can navigate ina new environment on the surface of another planet, etc.Machine learning incorporates methods and tools thathave their basis in different fields like pattern recognition,statistical learning theory, artificial neural networks, artificialintelligence, fuzzy logics, signal processing, and data mining.Pattern recognition is a key to handle all such problems.A pattern is a meaningful regularity or a structure and itforms the basis of recognition for any source, symbol orstructure. By the recognition of a pattern, we essentiallymean classifying it to its appropriate class. The capabilityof pattern recognition of the biological system is the ultimateof technology. The human perceptive power seems to bewell adapted to such pattern processing task. For example,recognition of individual speech, character of differentfonts in a very robust manner, recognising the entire tuneby observing just few whistle notes or identifying an oldfriend with a glimpse of the back of his head in a crowd.To understand the basis for this power in biological system,and consequently making an intelligent machine capableof performing the required recognition task involves thefollowing two steps.(i) Understanding the pattern recognition capability ofliving organisms, and(ii) Developing the theory and techniques for designingdevices capable of performing a given recognitiontask automatically.Human beings interpret the physical world with theinformation available to them in the form of some patterns.Mathematically, a pattern is an arrangement of d-real numbersin vector or matrix notation such as X=[x1, x2, x3 ,  , xd]where d is the number of features characterising eachpattern. In machine pattern recognition, the decision rulesbased on previous knowledge of the system are developedfor each pattern class and these rules are used for decidingclass-membership of unknown or new patterns.Pattern recognition methods and models have attractedthe researchers from the varied areas like biomedical andmedical diagnosis, handwriting analysis, image processing,speech recognition, signal processing, and characterrecognition. The technique has got wide applications inthe field of remote sensing, weather forecasting, forensicscience application, sensor technology, computer scienceand engineering, information theory, cryptology, militaryscience and many other areas of information retrieval anddecision-making. This indicates the importance of the subjectand the need for further development.All methods and models can be classified in two exclusivecategories. One is unsupervised pattern recognition orclustering, which uses only the proximity matrix to performthe classification and no category labels denoting a prioripartition of the objects are used, and the other is supervisedpattern recognition, where category labels on both theobjects as well as the proximity matrix are required. Theproblem is then to establish a discriminating surface thatseparates the objects according to category.In the present special issue, some of these technologieshave been presented in some review papers, research papers,and short communications, a brief outlines of which is asfollows.In the paper Temporal pattern classification usingkernal methods for speech recognition and speech emotionrecognition Chandra Sekhar and his co-author have proposedtwo models namely discrete hidden Markov models ( DHMMs)and continuous density hidden Markov models (CDHMMs)in the kernel and explicit kernel feature space extractedfrom temporal data and SVM as classifier for speech recognitionand speech emotion recognition problems. For recognition346of spoken letters, they have proposed a model using discretisedrepresentation and string kernel SVM-based classificationand observed that the performance is better than that ofthe model using continuous valued representation. Thescore vector-based approach and segment modelling-basedapproach have been used in a hybrid framework to modelthe sets of vectors-based representation to obtain a fixed-length pattern representation for a varying-length temporaldata and these used these for classification. GMM scorevector-based approach and the segment modelling-basedapproach are found to be much better for the task ofspeech emotion recognition.Jha and his co-author in the paper Development ofsurface acoustic wave electronic nose using patternrecognition system have modelled 11-element SAW sensor-based electronic nose to detect trinitrotoluene and dinitrotolueneexplosive vapours in the presence of toluene, benzene,dimethylphosphonate (DMMP) and humidity as interferents.They have very wisely used several denoising techniques,principal component analysis (PCA) for feature extractionand artificial neural network (ANN), K- nearest neighbour,Naïve Bayes, and SVM as classifier. The electronic nosesystems are very effective in detecting the hidden explosivesand can play an important role in homeland security andforensics. The life of soldiers can be saved in case ofchemical warfare. Developing pattern recognition systemfor each application with actual data is a costly affair, andhence, the proposed development of PR system for electronicnose with synthesised data is practical viability to searchfor appropriate sensors and effective data processing capabilitythat will reduce the cost and time of development.Singh and Yadav have proposed a new method fortransient feature extraction, which uses approximationcoefficients of discrete wavelet transform as input forPCA-based feature creation and selection in the paperFeatures extraction by wavelet decomposition of SAWsensor array transients. The above tools have been usedfor recognition of seven volatile organic compounds (VOCs)vapours: chloroform, chlorobenzene, O-dichlorobenzene,n-heptanes, toluene, n-hexane and n-octane. Synthetictransient response data have been generated by a prototype3-element polyisobutylene (PIB)-coated SAW sensor array.The analytical approach presented in the paper could bea new method for design and development of odour-sensingsystem based on SAW sensor array and pattern recognition.Gireesh Kumar, Poornaselvan, and Sethumadhavan inthe paper Fuzzy support vector machine-based multi-agentoptimal path planning approach to robot environmenthave dealt with the real-time navigation of a mobile robot.Fuzzy logic-based SVM have been proposed to achievea collision-free path for the robot. The proposal has beenvalidated in different unknown environments clutteredwith static and dynamic obstacles and the robots havereached safely the target.In the paper Boosting principal component analysisby genetic algorithm Somvanshi and Yadav have combinedPCA with GA to have a effective feature extraction method.PCA provides alternate feature spaces for data representation.All these features are fused and given a chromosomerepresentation. The genetic evolution of fused feature vectorprovides significant feature components (genes) as survivours.The neural network classifier has been used with thesefeatures and UCI repository data have been analysed. Theperformance of the proposed method has been found tobe better than the results reported in the literatures formost of the data analysed. The research work shows thatthe performance of PCA got boosted by GA.Manju Bala and Agrawal in the paper Statisticalmeasures to determine optimal structure decision tree-based one versus one support vector machine have proposedto solve multi-class problems, where optimal structureof decision tree is determined with the help of informationgain, gini index and chi-square. The performance of (OVO-ODT SVM) has been compared with (OVO SVM ) in termsof classification accuracy and computation time. It hasbeen observed that the proposed one is either comparableor better on various UCI repository data sets.In the paper Influence of terrain on modern tacticalcombat : Trust-based recommender system Bedi, et al.have designed and developed a prototype of trust-basedrecommender system for recommending to use a specifictype of weapon or a set of weapons that best suit in agiven type of terrain. They have used intuitionistic fuzzyset, from trustworthy pears and produced aggregated orderof recommendations taking degree of trust on recommendersinto consideration.In the paper Payload estimation in universal steganalysisthe authors have attempted to compute the embeddedpayload in the image by any of the six popular steganographicalgorithms, viz., JP Hides and Seek, PVD, LSB flipping,Outguess, S-Tool, and F5. Universal steganalysis can easilyrecognise the image with or without cover, but estimationof payload is an open problem. They have used PCAalong with support vector regression to estimates thepayload and support vector machine for classification ofsix algorithms. The detection accuracy and the quantitativesteganalysis are found to be better than that of universalsteganalysis.Saxena, et al. in their paper Index for garbledness forautomatic recognition of plain English texts have studiedthe characteristic of plain English language and extracted10 fuzzy features, based on which they have defined Indexof Garbledness to identify the plain English text even fromthe running character stream. It also provides the information,that to the extent the text is random. This tool can be usedeffectively to identify the plain English text from cipher text.Asthana and Verma in their paper Classification ofencrypted text and encrypted speech present the identification347of encrypted text and encrypted text bit stream from mixtureof cipher bit stream. This work is of much importance fromcryptanalysis point of view. They have taken various blocklength data for feature extraction and used well knownminimum distance classifier and maximum likelihood classifierfor identification.There is fair representation of tools and techniquesused in machine learning and pattern recognition in thepresent special issue. I am very grateful to all the authorswho have contributed research papers covering many aspectsof this widely used technology.I am indeed grateful to the reviewers for their time andproviding expert comments for the improvements and revisionsof these papers by the authors. I am also grateful to Editor-in-Chief for prompt approval of my proposal to bring outa special issue on state-of-the-art topics on machine learningand pattern recognition for defence science problem.Finally, I would like to thank Director, DESIDOC andhis team for their untiring efforts in bringing out thisspecial issue.Dr Shri KantCoordinatorJoint Cipher BureauDeptt of Defence R&DMetcalfe House, Delhi.

Machine Learning and Pattern Recognition

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Machine Learning and Pattern Recognition

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