Abstract: The goal of the paper is to investigate what training sample estimate of misclassification probability would be the best one for the histogram classifier. Certain quality criterion is suggested. The deviation for some estimates, such as resubstitution error (empirical risk), cross validation error (leave-one-out), bootstrap and for the best estimate obtained via some optimization procedure, is calculated and compared for some examples.

Keywords: pattern recognition, classification, statistical robustness, deciding functions, complexity, capacity, overfitting, overtraining problem.

ACM Classification Keywords: G.3 Probability and statistics, G.1.6. Numerical analysis: Optimization; G.2.m. Discrete mathematics: miscellaneous.

Link:

ОПТИМИЗАЦИЯ ОЦЕНКИ ВЕРОЯТНОСТИ ОШИБОЧНОЙ КЛАССИФИКАЦИИ В ДИСКРЕТНОМ СЛУЧАЕ1

Виктор Неделько

http://foibg.com/ibs_isc/ibs-08/ibs-08-p07.pdf

Аннотация: Рассматриваются дискретные экстремальные задачи, к которым сводятся некоторые варианты проблемы помехоустойчивого off-line обнаружения в числовой последовательности повторяющегося фрагмента, а также некоторые варианты проблемы поиска подмножеств векторов во множестве векторов евклидова пространства. Анализируется сложность редуцированных оптимизационных задач и соответствующих им задач анализа данных и распознавания образов. Дан обзор новых и известных алгоритмических результатов по решению этих задач.

Ключевые слова: поиск подмножеств векторов, помехоустойчивое обнаружение повторяющегося фрагмента, кластерный анализ, дискретная оптимизация, NP-трудная задача, алгоритмы с гарантированными оценками точности.

ACM Classification Keywords: F.2. Analysis of Algorithms and Problem Complexity, G.1.6. Optimization, G2. Discrete Mathematics, I.5.3. Pattern Recognition: Clustering.

Link:

О НЕКОТОРЫХ ТРУДНОРЕШАЕМЫХ ЗАДАЧАХ ПОМЕХОУСТОЙЧИВОГО АНАЛИЗА СТРУКТУРИРОВАННЫХ ДАННЫХ1

Александр Кельманов

http://foibg.com/ibs_isc/ibs-08/ibs-08-p06.pdf

Аннотация. Рассматривается постановка задачи и процедура векторной оптимизации архитектуры нейросетевого классификатора. В качестве целевой функции предложена скалярная свертка критериев по нелинейной схеме компромиссов. Используются поисковые методы оптимизации с дискретными аргументами. Приведен пример – нейросетевой классификатор текстов.

Ключевые слова: многокритериальная оптимизация, нейронные сети, классификатор.

ACM Classification Keywords: H.1 Models and Principles – H.1.1 – Systems and Information Theory; H.4.2 – Types of Systems; C.1.3 Other Architecture Styles – Neural nets

Link:

МНОГОКРИТЕРИАЛЬНАЯ ОПТИМИЗАЦИЯ АРХИТЕКТУРЫ НЕЙРОСЕТЕВЫХ КЛАССИФИКАТОРОВ

Альберт Воронин, Юрий Зиатдинов, Анна Антонюк

http://foibg.com/ibs_isc/ibs-08/ibs-08-p05.pdf

Abstract: This paper shows some ideas about how to incorporate a string learning stage in self-organizing algorithms. T. Kohonen and P. Somervuo have shown that self-organizing maps (SOM) are not restricted to numerical data. This paper proposes a symbolic measure that is used to implement a string self-organizing map based on SOM algorithm. Such measure between two strings is a new string. Computation over strings is performed using a priority relationship among symbols; in this case, symbolic measure is able to generate new symbols. A complementary operation is defined in order to apply such measure to DNA strands. Finally, an algorithm is proposed in order to be able to implement a string self-organizing map.

Keywords: Neural Network, Self-organizing Maps, and Control Feedback Methods.

ACM Classification Keywords: F.1.1 Models of Computation: Self-modifying machines (neural networks); F.1.2 Modes of Computation: Alternation and non-determinism.

Link:

STRING MEASURE APPLIED TO STRING SELF-ORGANIZING MAPS AND NETWORKS OF EVOLUTIONARY PROCESSORS1

Nuria Gómez Blas, Luis F. de Mingo, Francisco Gisbert, Juan M. Garitagoitia

Abstract: A major drawback of artificial neural networks is their black-box character. Therefore, the rule extraction algorithm is becoming more and more important in explaining the extracted rules from the neural networks. In this paper, we use a method that can be used for symbolic knowledge extraction from neural networks, once they have been trained with desired function. The basis of this method is the weights of the neural network trained. This method allows knowledge extraction from neural networks with continuous inputs and output as well as rule extraction. An example of the application is showed. This example is based on the extraction of average load demand of a power plant.

Keywords: Neural Network, Backpropagation, Control Feedback Methods.

ACM Classification Keywords: F.1.1 Models of Computation: Self-modifying machines (neural networks); F.1.2 Modes of Computation: Alternation and nondeterminism.

Link:

CLASSIFICATION OF DATA TO EXTRACT KNOWLEDGE FROM NEURAL NETWORKS

Ana Martinez, Angel Castellanos, Rafael Gonzalo

Abstract: Some aspects of design of the discriminant functions that in the best way separate points of predefined final sets are considered. The concept is introduced of the nested discriminant functions which allow to separate correctly points of any of the final sets. It is proposed to apply some methods of non-smooth optimization to solve arising extremal problems efficiently.

Keywords: cluster, solving rule, discriminant function, linear and non-linear programming, non-smooth optimization

ACM Classification Keywords: G.1.6 Optimization - Gradient methods, I.5 Pattern Recognition; I.5.2 Design Methodology - Classifier design and evaluation

Link:

EXACT DISCRIMINANT FUNCTION DESIGN USING SOME OPTIMIZATION TECHNIQUES

Yury Laptin, Alexander Vinogradov

http://foibg.com/ibs_isc/ibs-08/ibs-08-p02.pdf

Abstract: The task of smooth and stable decision rules construction in logical recognition models is considered. Logical regularities of classes are defined as conjunctions of one-place predicates that determine the membership of features values in an intervals of the real axis. The conjunctions are true on a special no extending subsets of reference objects of some class and are optimal. The standard approach of linear decision rules construction for given sets of logical regularities consists in realization of voting schemes. The weighting coefficients of voting procedures are done as heuristic ones or are as solutions of complex optimization task. The modifications of linear decision rules are proposed that are based on the search of maximal estimations of standard objects for their classes and use approximations of logical regularities by smooth sigmoid functions.

Keywords: precedent-recognition recognition, logical regularities of classes, estimate calculation algorithms, integer programming, decision rules, sigmoid formatting rules

Link:

OPTIMAL DECISION RULES IN LOGICAL RECOGNITION MODELS

Anatol Gupal, Vladimir Ryazanov

http://foibg.com/ibs_isc/ibs-08/ibs-08-p01.pdf

Abstract: Composition problem is considered for partition constrained vertex subsets of n dimensional unit cube En . Generating numerical characteristics of En subsets partitions is considered by means of the same characteristics in n −1 dimensional unit cube, and construction of corresponding subsets is given for a special particular case. Using pairs of lower layer characteristic vectors for En−1 more characteristic vectors for En are composed which are boundary from one side, and which take part in practical recognition of validness of a given candidate vector of partitions.

Keywords: monotone Boolean functions, (0,1)-matrices

ACM Classification Keywords: G.2.1 Discrete mathematics: Combinatorics

Link:

GENERATING MORE BOUNDARY ELEMENTS OF SUBSET PROJECTIONS

Hasmik Sahakyan, Levon Aslanyan

http://foibg.com/ibs_isc/ibs-09/ibs-09-p18.pdf

Abstract: In this paper, a novel approach for character recognition has been presented with the help of genetic operators which have evolved from biological genetics and help us to achieve highly accurate results. A genetic algorithm approach has been described in which the biological haploid chromosomes have been implemented using a single row bit pattern of 315 values which have been operated upon by various genetic operators. A set of characters are taken as an initial population from which various new generations of characters are generated with the help of selection, crossover and mutation. Variations of population of characters are evolved from which the fittest solution is found by subjecting the various populations to a new fitness function developed. The methodology works and reduces the dissimilarity coefficient found by the fitness function between the character to be recognized and members of the populations and on reaching threshold limit of the error found from dissimilarity, it recognizes the character. As the new population is being generated from the older population, traits are passed on from one generation to another. We present a methodology with the help of which we are able to achieve highly efficient character recognition.

Keywords: Genetic operators, character recognition, genetics, genetic algorithm.

ACM Classification Keywords: I.2 Artificial Intelligence, I.4 Image processing and computer vision, I.5 Pattern Recognition.

Link:

SELF EVOLVING CHARACTER RECOGNITION USING GENETIC OPERATORS

Shashank Mathur

http://foibg.com/ibs_isc/ibs-09/ibs-09-p17.pdf

Abstract: The problem of transit points arrangement is presented in the paper. This issue is connected with accuracy of tariff distance calculation and it is the urgent problem at present. Was showed that standard method of tariff distance discovering is not optimal. The Genetic Algorithms are used in optimization problem resolution. The UML application class diagram and class content are showed. In the end the example of transit points arrangement is represented.

Keywords: transit points arrangement, genetic algorithms, optimization, software application.

ACM Classification Keywords: G.1.6 Optimization - Global optimization, D.1.1 Applicative (Functional) Programming

Link:

IMPLEMENTATION OF GENETIC ALGORITHMS FOR TRANSIT POINTS ARRANGEMENT

Dmitry Panchenko, Maxim Shcherbakov

http://foibg.com/ibs_isc/ibs-09/ibs-09-p16.pdf

Abstract: Membrane computing is a recent area that belongs to natural computing. This field works on computational models based on nature's behavior to process the information. Recently, numerous models have been developed and implemented with this purpose. P-systems are the structures which have been defined, developed and implemented to simulate the behavior and the evolution of membrane systems which we find in nature. What we show in this paper is an application capable to simulate the P-systems based on a multiagent systems (MAS) technology. The main goal we want to achieve is to take advantage of the inner qualities of the multiagent systems. This way we can analyse the proper functioning of any given p-system. When we observe a P-system from a different perspective, we can be assured that it is a particular case of the multiagent systems. This opens a new possibility, in the future, to always evaluate the P-systems in terms of the multiagent systems technology.

Keywords: P-systems mapping, multiagent systems.

Link:

ANALYSIS OF P-SYSTEMS UNDER A MULTIAGENT SYSTEMS PERSPECTIVE

Alberto Arteta, Angel Goñi, Juan Castellanos

http://foibg.com/ibs_isc/ibs-09/ibs-09-p15.pdf

Abstract: in the paper learning algorithm for adjusting weight coefficients of the Cascade Neo-Fuzzy? Neural Network (CNFNN) in sequential mode is introduced. Concerned architecture has the similar structure with the Cascade-Correlation? Learning Architecture proposed by S.E. Fahlman and C. Lebiere, but differs from it in type of artificial neurons. CNFNN consists of neo-fuzzy neurons, which can be adjusted using high-speed linear learning procedures. Proposed CNFNN is characterized by high learning rate, low size of learning sample and its operations can be described by fuzzy linguistic “if-then” rules providing “transparency” of received results, as compared with conventional neural networks. Using of online learning algorithm allows to process input data sequentially in real time mode.

Keywords: artificial neural networks, constructive approach, fuzzy inference, hybrid systems, neo-fuzzy neuron, real-time processing, online learning.

ACM Classification Keywords: I.2.6 Learning – Connectionism and neural nets

THE CASCADE NEO-FUZZY ARCHITECTURE AND ITS ONLINE LEARNING ALGORITHM

Yevgeniy Bodyanskiy, Yevgen Viktorov

http://foibg.com/ibs_isc/ibs-09/ibs-09-p14.pdf

Abstract: In this paper an evolutionary algorithm is proposed for solving the problem of production scheduling in assembly system. The aim of the paper is to investigate a possibility of the application of evolutionary algorithms in the assembly system of a normally functioning enterprise producing household appliances to make the production graphic schedule.

Keywords: Artificial intelligence, flexible assembly systems, evolutionary algorithm, production scheduling.

ACM Classification Keywords: I. Computing methodologies I.1.Symbolic and algebraic manipulation I.1.3.Evaluation strategies I.2.Artificial Intelligence I.2.8.Problem solving Control Methods and Search – Scheduling J.6.Computer Aided Engineering - Computer Aided Manufacturing (CAM).

Link:

THE PRODUCTION SCHEDULING IN ASSEMBLY SYSTEM WITH EVOLUTIONARY ALGORITHM

Galina Setlak

http://foibg.com/ibs_isc/ibs-09/ibs-09-p13.pdf

Аннотация: Развит подход к рассуждениям по аналогии для иерархически структурированных описаний эпизодов, ситуаций и их компонентов на базе представлений аналогов в виде особой формы векторных представлений - распределенных кодвекторных представлений. Предложены распределенные представления компонентов аналогов, позволяющие непосредственно определять соответствующие друг другу представления компонентов для реализации стадии отображения двух аналогов, а также метод вывода по аналогии на их основе. Предложенные методы исследованы на базах аналогий, которые ранее применялись для исследования ведущих моделей аналогии - SME и ACME. Полученные результаты находятся на уровне результатах SME и ACME, однако за счет использования сходства векторных представления обладают низкой вычислительной сложностью и создают основу для более адекватного учета семантики аналогов и их компонентов. Это делает предложенные методы перспективными для отображения фрагментов баз знаний с большим числом компонентов.

Ключевые слова: аналогия, отображение аналогов, вывод по аналогии, распределенное представление информации, кодвекторы, базы знаний, SME, ACME

ACM Classification Keywords: I.2 Artificial Intelligence, I.2.4 Knowledge Representation Formalisms and Methods, I.2.6 Learning (Analogies)

Link:

ОТОБРАЖЕНИЕ И ВЫВОД ПО АНАЛОГИИ НА ОСНОВЕ НЕЙРОСЕТЕВЫХ РАСПРЕДЕЛЕННЫХ ПРЕДСТАВЛЕНИЙ

Сергей Слипченко, Дмитрий Рачковский

http://foibg.com/ibs_isc/ibs-09/ibs-09-p12.pdf

Аннотация: В данной работе исследуется применение нейронной сети Хемминга для обнаружения краёв объектов на изображении. Изображение в оттенках серого, поступающее на вход предлагаемой системы, подвергается преобразованию с применением нечёткой логики в двуцветное. После этого из изображения последовательно выделяются блоки пикселей заданной размерности и подаются на входы предварительно инициализированной сети Хемминга. Нейронная сеть выполняет идентификацию краёв в блоке, и в новом изображении вставляет на место блока шаблон, который отвечает коду, полученному на выходе сети. Работу завершает практическое применение метода.

Ключевые слова: Детектирование краёв объектов изображения, Метод разностного группирования, Нейронная сеть Хемминга, Нечёткая логика.

ACM Classification Keywords: I.4.3. Enhancement – Grayscale manipulation, I.4.6. Segmentation – Edge and feature detection, I.4.6. Segmentation – Pixel classification.

Link:

ПРИМЕНЕНИЕ НЕЙРОННОЙ СЕТИ ХЕММИНГА И НЕЧЕТКОЙ ЛОГИКИ К ОБНАРУЖЕНИЮ КРАЕВ ОБЪЕКТОВ НА ИЗОБРАЖЕНИЯХ В ОТТЕНКАХ СЕРОГО

Николай Мурга

http://foibg.com/ibs_isc/ibs-09/ibs-09-p11.pdf

Аннотация: В статье представлена инструментальная среда для исследования эволюционных стратегий, которые используют механизм аппроксимации целевой функции с помощью аппарата нейронных сетей. Приводится описание алгоритма эволюционной стратегии и подходы к построению метамоделей. Рассмотрены существующие на сегодняшний день алгоритмы оптимизации на основе эволюционных стратегий и метамоделей. Отмечается актуальность применения механизмов адаптации в таких алгоритмах. Описаны основные функции и возможности инструментальной среды. Средством реализации является программный пакет MatLab? v.7.1.

Ключевые слова: инструментальная среда, эволюционная стратегия, нейронная сеть, метамодель, оптимизация.

Link:

ИНСТРУМЕНТАЛЬНАЯ СРЕДА ДЛЯ ИССЛЕДОВАНИЯ ЭВОЛЮЦИОННЫХ СТРАТЕГИЙ С ИСПОЛЬЗОВАНИЕМ НЕЙРОСЕТЕВЫХ МЕТАМОДЕЛЕЙ

Павел Афонин

http://foibg.com/ibs_isc/ibs-09/ibs-09-p10.pdf

Abstract: The problem of cancer diagnosis from multi-channel images using the neural networks is investigated. The goal of this work is to classify the different tissue types which are used to determine the cancer risk. The radial basis function networks and backpropagation neural networks are used for classification. The results of experiments are presented.

Keywords: neural networks, backpropagation, RBF, uterine cervix, cancer, classification.

ACM Classification Keywords: I.5.1 Pattern Recognition - Neural nets

Link:

THE USAGE OF NEURAL NETWORKS FOR THE MEDICAL DIAGNOSIS

Kateryna Malyshevska

http://foibg.com/ibs_isc/ibs-09/ibs-09-p09.pdf

Abstract: In the world, scientific studies increase day by day and computer programs facilitate the human’s life. Scientists examine the human’s brain’s neural structure and they try to be model in the computer and they give the name of artificial neural network. For this reason, they think to develop more complex problem’s solution. The purpose of this study is to estimate fuel economy of an automobile engine by using artificial neural network (ANN) algorithm. Engine characteristics were simulated by using “Neuro Solution” software. The same data is used in MATLAB to compare the performance of MATLAB is such a problem and show its validity. The cylinder, displacement, power, weight, acceleration and vehicle production year are used as input data and miles per gallon (MPG) are used as target data. An Artificial Neural Network model was developed and 70% of data were used as training data, 15% of data were used as testing data and 15% of data is used as validation data. In creating our model, proper neuron number is carefully selected to increase the speed of the network. Since the problem has a nonlinear structure, multi layer are used in our model.

Keywords: Artificial Neural Network, Fuel Economy

Link:

PERFORMANCE COMPARISON OF MATLAB AND NEURO SOLUTION SOFTWARE ON ESTIMATION OF FUEL ECONOMY BY USING ARTIFICIAL NEURAL NETWORK

Hakan Serhad Soyhan, Mehmet Emre Kilic, Burak Gokalp, Imdat Taymaz

http://foibg.com/ibs_isc/ibs-09/ibs-09-p08.pdf

Abstract: Architecture and learning algorithm of self-learning spiking neural network in fuzzy clustering task are outlined. Fuzzy receptive neurons for pulse-position transformation of input data are considered. It is proposed to treat a spiking neural network in terms of classical automatic control theory apparatus based on the Laplace transform. It is shown that synapse functioning can be easily modeled by a second order damped response unit. Spiking neuron soma is presented as a threshold detection unit. Thus, the proposed fuzzy spiking neural network is an analog-digital nonlinear pulse-position dynamic system. It is demonstrated how fuzzy probabilistic and possibilistic clustering approaches can be implemented on the base of the presented spiking neural network.

Keywords: computational intelligence, hybrid intelligent system, spiking neural network, fuzzy receptive neuron, fuzzy clustering, automatic control theory, analog-digital system, second order damped response system.

ACM Classification Keywords: I.2.6 Artificial Intelligence: Learning – Connectionism and neural nets; I.2.8 Artificial Intelligence: Problem Solving, Control Methods, and Search – Control theory; I.5.1 Pattern Recognition: Models – Fuzzy set, Neural nets; I.5.3 Pattern Recognition: Clustering – Algorithms.

Link:

SELF-LEARNING FUZZY SPIKING NEURAL NETWORK AS A NONLINEAR PULSE-POSITION THRESHOLD DETECTION DYNAMIC SYSTEM BASED ON SECOND-ORDER CRITICALLY DAMPED RESPONSE UNITS

Yevgeniy Bodyanskiy, Artem Dolotov, Iryna Pliss

http://foibg.com/ibs_isc/ibs-09/ibs-09-p07.pdf

Аннотация: Предложен новый подход к понятию «смысл» и дано его формально-графическое определение через онтологию. Рассмотрена задача семантического (смыслового) анализа текстов ЕЯ, которая основана на процедуре поиске подграфа концептуального графа, отображающего знания о мире.

Ключевые слова: онтология, смысл, текст, семантический анализ.

ACM Classification Keywords: 1.2.4 Knowledge Representation Formalisms and Methods

Link:

ОПРЕДЕЛЕНИЕ ПОНЯТИЯ «СМЫСЛ» ЧЕРЕЗ ОНТОЛОГИЮ. СЕМАНТИЧЕСКИЙ АНАЛИЗ ТЕКСТОВ ЕСТЕСТВЕННОГО ЯЗЫКА

Леонид Святогор, Виктор Гладун

http://foibg.com/ibs_isc/ibs-09/ibs-09-p06.pdf

Аннотация: Описывается один из подходов к анализу естественно-языкового текста, который использует толковый словарь естественного языка, локальный словарь анализируемого текста и частотные характеристики слов в этом тексте.

Ключевые слова: представление текста, обработка текста, формальная логическая система.

ACM Classification Keywords: I.2.4 Knowledge Representation Formalisms and Methods - Representation languages, I.2.7 Natural Language Processing - Language models

Link:

ОБРАБОТКА ПРЕДЛОЖЕНИЙ ЕСТЕСТВЕННОГО ЯЗЫКА С ИСПОЛЬЗОВАНИЕМ СЛОВАРЕЙ И ЧАСТОТЫ ПОЯВЛЕНИЯ СЛОВ

Александр Палагин, Сергей Крывый, Дмитрий Бибиков

http://foibg.com/ibs_isc/ibs-09/ibs-09-p05.pdf

Аннотация: Рассматриваются проблемы анализа естественно-языковых объектов (ЕЯО) с точки зрения их представления и обработки в памяти компьютера. Предложена формализация задачи анализа ЕЯО и приведен пример формализованного представления ЕЯО предметной области.

Ключевые слова: термины предметной области, формальная логическая система, онтология.

ACM Classification Keywords: I.2.4 Knowledge Representation Formalisms and Methods - Representation languages, I.2.7 Natural Language Processing - Language models

Link:

К АНАЛИЗУ ЕСТЕСТВЕННО-ЯЗЫКОВЫХ ОБЪЕКТОВ

Александр Палагин, Сергей Крывый, Виталий Величко, Николай Петренко

http://foibg.com/ibs_isc/ibs-09/ibs-09-p04.pdf

Abstract: The given work is devoted to development of the computer-aided system of semantic text analysis of a technical specification. The purpose of this work is to increase efficiency of software engineering based on automation of semantic text analysis of a technical specification. In work it is offered and investigated a technique of the text analysis of a technical specification is submitted, the expanded fuzzy attribute grammar of a technical specification, intended for formalization of limited Russian language is constructed with the purpose of analysis of offers of text of a technical specification, style features of the technical specification as class of documents are considered, recommendations on preparation of text of a technical specification for the automated processing are formulated. The computer-aided system of semantic text analysis of a technical specification is considered. This system consist of the following subsystems: preliminary text processing, the syntactic and semantic analysis and construction of software models, storage of documents and interface.

Keywords: natural language, semantic text analysis, technical specification.

ACM Classification Keywords: I.2.7 Natural Language Processing

Link:

COMPUTER SUPPORT OF SEMANTIC TEXT ANALYSIS OF A TECHNICAL SPECIFICATION ON DESIGNING SOFTWARE

Alla Zaboleeva-Zotova?, Yulia Orlova

http://foibg.com/ibs_isc/ibs-09/ibs-09-p03.pdf

Abstract: Mobile phones have the potential of fostering political mobilisation. There is a significant political power in mobile technology. Like the Internet, mobile phones facilitate communication and rapid access to information. Compared to the Internet, however, mobile phone diffusion has reached a larger proportion of the population in most countries, and thus the impact of this new medium is conceivably greater. There are now more mobile phones in the UK than there are people (averaging at 121 mobile phones for every 100 people). In this paper, the attempt to use modern mobile technology to handle the General Election, is discussed. The pre-election advertising, election day issues, including the election news and results as they come in, and answering questions via text message regarding the results of current and/or previous general elections are considered.

Keywords: mobile text messages, mobile election, mobile advertising, question-answering system

ACM Classification Keywords: I.2 Artificial intelligence: I.2.7 Natural Language Processing: Text analysis.

Link:

MOBILE ELECTION

Elena Long, Vladimir Lovitskii, Michael Thrasher, David Traynor

http://foibg.com/ibs_isc/ibs-09/ibs-09-p02.pdf

Abstract: Mobile advertising is a rapidly growing sector providing brands and marketing agencies the opportunity to connect with consumers beyond traditional and digital media and instead communicate directly on their mobile phones. Mobile advertising will be intrinsically linked with mobile search, which has transported from the internet to the mobile and is identified as an area of potential growth. The result of mobile searching show that as a general rule such search result exceed 160 characters; the dialog is required to deliver the relevant portion of a response to the mobile user. In this paper we focus initially on mobile search and mobile advert creation, and later the mechanism of interaction between the user’s request, the result of searching, advertising and dialog.

Keywords: mobile text messages, mobile search, mobile advertising, question-answering system

ACM Classification Keywords: I.2 Artificial intelligence: I.2.7 Natural Language Processing: Text analysis.

Link:

MOBILE SEARCH AND ADVERTISING

Vladimir Lovitskii, Colin McCaffery?, Michael Thrasher, David Traynor, Peter Wright

http://foibg.com/ibs_isc/ibs-09/ibs-09-p01.pdf