28084197
Dynamic category profiling for text filtering and classification
Information is often represented in text form and classified into categories. Unfortunately, automatic classifiers often conduct misclassifications. One of the reasons is that the documents for training the classifiers are mainly from the categories, leading the classifiers to derive category profiles for distinguishing each category from others, rather than measuring the extent to which a document's content overlaps that of a category. To tackle the problem, we present a technique DP4FC that selects suitable features to construct category profiles to distinguish relevant documents from irrelevant documents. More specially, DP4FC is associated with various classifiers. Upon receiving a document, it helps the classifiers to create dynamic category profiles with respect to the document, and accordingly make proper decisions in filtering and classification. Theoretical analysis and empirical results show that DP4FC may significantly promote different classifiers' performances under various environments.
43626831
Parsing-Driven Generalization for Natural Language Acquisition
Parsing is an important step in natural language processing. It involves tasks of searching for applicable grammatical rules which can transform natural language sentences into their corresponding parse trees. Therefore parsing can be viewed as problem solving, and language acquisition can be achieved by generalizing problem solving heuristics. In this paper we investigate how machine learning methodologies can be integrated with a Wait-And-See Parser (the problem solver) to acquire parsing-related knowledge that is needed for the parser. We call this approach parsing-driven generalization since learning (acquisition of parsing rules and classification of lexicons) is basically derived from the parsing process. Three types of generalization are reported in this paper: simple generalization, generalization by asking questions, and generalization back-propagation. Simple generalization generalizes any two parsing rules whose action parts (right-hand sides) are the same but whose condition parts (left-hand sides) have a single difference. Generalization by asking questions is triggered when a “climbing-up” move on a concept hierarchy is attempted. It is necessary for avoiding over-generalization. Generalization back-propagation propagates a confirmed generalization of some later parsing rule back to its precedent rules in a parsing sequence and thus causes them to be generalized as well. It can reduce the number of questions asked by the system. With these three types of generalization and a mechanism for maintaining lexicon classification (the domain concept hierarchy), parsing and learning can interact to utilize and acquire parsing-related knowledge. To promote the practical performance of parsing after learning, a relaxation parsing mechanism is also designed to process unseen sentences.
40646883
Adaptive sampling for thresholding in document filtering and classification
Document filtering (DF) and document classification (DC) are often integrated together to classify suitable documents into suitable categories. A popular way to achieve integrated DF and DC is to associate each category with a threshold. A document d may be classified into a category c only if its degree of acceptance (DOA) with respect to c is higher than the threshold of c. Therefore, tuning a proper threshold for each category is essential. A threshold that is too high (low) may mislead the classifier to reject (accept) too many documents. Unfortunately, thresholding is often based on the classifier's DOA estimations, which cannot always be reliable, due to two common phenomena: (1) the DOA estimations made by the classifier cannot always be correct, and (2) not all documents may be classified without any controversy. Unreliable estimations are actually noises that may mislead the thresholding process. In this paper, we present an adaptive and parameter-free technique AS4T to sample reliable DOA estimations for thresholding. AS4T operates by adapting to the classifier's status, without needing to define any parameters. Experimental results show that, by helping to derive more proper thresholds, AS4T may guide various classifiers to achieve significantly better and more stable performances under different circumstances. The contributions are of practical significance for real-world integrated DF and DC.
8117936
Adaptive Agents for Effective Information Monitoring
Information motoring is an essential basis for management and decision making in various domains. Once an information update is detected, suitable procedures may be triggered to identify potential problems and opportunities. In this paper, we define effective information monitoring (EIM) and explore how adaptive agents may cooperate with each other in order to achieve their collective goal of EIM. Since each information item may be updated by various entities (e.g. information servers) at any time, EIM calls for a multiagent system that may detect more information updates in a timely manner using a controlled amount of system resources (e.g. loading of related information servers and the Intranet). To achieve EIM, the agents should adapt themselves to the dynamically changing behaviors of the information items being monitored. They learn to issue requests of using system resources and concede to those agents that are more likely to detect updates at the time of negotiation. The framework is theoretically ...
22077737
Collaborative Multiagent Adaptation for Business Environmental Scanning Through the Internet
Businesses should promptly respond to their dynamic environments. Environmental scanners are thus essential for the businesses to discover and monitor environmental information of interest (IOI). In this paper, we explore user-centered, continuous and resource-bounded environmental scanning (UCRES). Upon receiving information preferences of managers, new IOI should be continuously detected in a timely and complete manner without consuming too much resource (e.g. bandwidths of computer networks and services of information servers). We develop a multiagent framework AESA to tackle the challenges of UCRES. Each agent is a simple entity. All agents collaboratively adapt their population and resource consumption to several dynamic aspects of UCRES: information preferences of individual users, resource limitation of environmental scanning, distribution of IOI in the environments, and update behaviors of the IOI. The delivery of AESA to businesses may constantly provide a larger amount of important and timely IOI without exhausting the Intranet and the Internet communities.
9652055
Incremental context mining for adaptive document classification
Automatic document classification (DC) is essential for the management of information and knowledge. This paper explores two practical issues in DC: (1) each document has its context of discussion, and (2) both the content and vocabulary of the document database is intrinsically evolving. The issues call for adaptive document classification (ADC) that adapts a DC system to the evolving contextual requirement of each document category, so that input documents may be classified based on their contexts of discussion. We present an incremental context mining technique to tackle the challenges of ADC. Theoretical analyses and empirical results show that, given a text hierarchy, the mining technique is efficient in incrementally maintaining the evolving contextual requirement of each category. Based on the contextual requirements mined by the system, higher-precision DC may be achieved with better efficiency.
27925843
Chromaticity of a family of K4-homeomorphs
Abstract We discuss the chromaticity of one family of K 4 -homeomorphs which has exactly 2 adjacent paths of length 1, and give sufficient and necessary condition for the graphs in the family to be chromatically unique.
18331520
Reliable channel regions for good binary codes transmitted over parallel channels
We study the average error probability performance of binary linear code ensembles when each codeword is divided into J subcodewords with each being transmitted over one of J parallel channels. This model is widely accepted for a number of important practical channels and signaling schemes including block-fading channels, incremental redundancy retransmission schemes, and multicarrier communication techniques for frequency-selective channels. Our focus is on ensembles of good codes whose performance in a single channel model is characterized by a threshold behavior, e.g., turbo and low-density parity-check (LDPC) codes. For a given good code ensemble, we investigate reliable channel regions which ensure reliable communications over parallel channels under maximum-likelihood (ML) decoding. To construct reliable regions, we study a modifed 1961 Gallager bound for parallel channels. By allowing codeword bits to be randomly assigned to each component channel, the average parallel-channel Gallager bound is simplified to be a function of code weight enumerators and channel assignment rates. Special cases of this bound, average union-Bhattacharyya (UB), Shulman-Feder (SF), simplified-sphere (SS), and modified Shulman-Feder (MSF) parallel-channel bounds, allow for describing reliable channel regions using simple functions of channel and code spectrum parameters. Parameters describing the channel are the average parallel-channel Bhattacharyya noise parameter, the average channel mutual information, and parallel Gaussian channel signal-to-noise ratios (SNRs). Code parameters include the union-Bhattacharyya noise threshold and the weight spectrum distance to the random binary code ensemble. Reliable channel regions of repeat-accumulate (RA) codes for parallel binary erasure channels (BECs) and of turbo codes for parallel additive white Gaussian noise (AWGN) channels are numerically computed and compared with simulation results based on iterative decoding. In addition, an examp
37263695
A complete solution to a conjecture on chromatic uniqueness of complete tripartite graphs
Let P(G,@l) be the chromatic polynomial of a graph G. A graph G is chromatically unique if for any graph H, P(H,@l)=P(G,@l) implies [email protected]?G. Koh, Teo and Chia conjectured that for any integers n and k with n>=k+2>=4, the complete tripartite graph K(n-k,n,n) is chromatically unique. Let K(n,m,r)-S denote the graph obtained by deleting all edges in S from the complete tripartite K(n,m,r). In this paper, we establish that for any positive integer n>=m>=r>=2, the chromatic equivalence class of K(n,m,r) is contained in the family {K(x,y,z)-S|1==2k and k>=2.
40972997
Algorithmic Aspects of Topology Control Problems for Ad Hoc Networks
Topology control problems are concerned with the assignment of power values to the nodes of an ad hoc network so that the power assignment leads to a graph topology satisfying some specified properties. This paper considers such problems under several optimization objectives, including minimizing the maximum power and minimizing the total power. A general approach leading to a polynomial algorithm is presented for minimizing maximum power for a class of graph properties called monotone properties. The difficulty of generalizing the approach to properties that are not monotone is discussed. Problems involving the minimization of total power are known to be NP-complete even for simple graph properties. A general approach that leads to an approximation algorithm for minimizing the total power for some monotone properties is presented. Using this approach, a new approximation algorithm for the problem of minimizing the total power for obtaining a 2-node-connected graph is developed. It is shown that this algorithm provides a constant performance guarantee. Experimental results from an implementation of the approximation algorithm are also presented.
1352172
Spectral Sequencing Based on Graph Distance
The construction of linear mesh layouts has found various applications, such as implicit mesh filtering and mesh streaming, where a variety of layout quality criteria, e.g., span and width, can be considered. While spectral sequencing, derived from the Fiedler vector, is one of the best-known heuristics for minimizing width, it does not perform as well as the Cuthill-Mckee (CM) scheme in terms of span. In this paper, we treat optimal mesh layout generation as a problem of preserving graph distances and propose to use the subdominant eigenvector of a kernel (affinity) matrix for sequencing. Despite the non-sparsity of the affinity operators we use, the layouts can be computed efficiently for large meshes through subsampling and eigenvector extrapolation. Our experiments show that the new sequences obtained outperform those derived from the Fiedler vector, in terms of spans, and those obtained from CM, in terms of widths and other important quality criteria. Therefore, in applications where several such quality criteria can influence algorithm performance simultaneously, e.g., mesh streaming and implicit mesh filtering, the new mesh layouts could potentially provide a better trade-off.