14475371
The Perceptron Algorithm with Uneven Margins
The perceptron algorithm with margins is a simple, fast and effective learning algorithm for linear classifiers; it produces decision hyperplanes within some constant ratio of the maximal margin. In this paper we study this algorithm and a new variant: the perceptron algorithm with uneven margins, tailored for document categorisation problems (i.e. problems where classes are highly unbalanced and performance depends on the ranking of patterns). We discuss the interest of these algorithms from a theoretical point of view, provide a generalisation of Novikoff’s theorem for uneven margins, give a geometrically description of these algorithms and show experimentally that both algorithms yield equal or better performances than support vector machines, while reducing training time and sparsity, in classification (USPS) and document categorisation (Reuters) problems.
5886737
Using KCCA for Japanese–English cross-language information retrieval and document classification
Kernel Canonical Correlation Analysis (KCCA) is a method of correlating linear relationship between two variables in a kernel defined feature space. A machine learning algorithm based on KCCA is studied for cross-language information retrieval. We apply the algorithm in Japanese–English cross-language information retrieval. The results are quite encouraging and are significantly better than those obtained by other state of the art methods. Computational complexity is an important issue when applying KCCA to large dataset as in information retrieval. We experimentally evaluate several methods to alleviate the problem of applying KCCA to large datasets. We also investigate cross-language document classification using KCCA as well as other methods. Our results show that it is feasible to use a classifier learned in one language to classify the documents in other languages.
6066153
Kernel Methods for Document Filtering
This paper describes the algorithms inplemented by the KerMIT consortium for its participation in the Trec 2002 Filtering track. The consortium submitted runs for the routing task using a linear SVM, for the batch task using the same SVM in combination with an innovation threshold-selection mechanism, and for the adaptive task using both a second-order perceptron and a combination of SVM and perceptron with uneven margin. Results seem to indicate that these algorithm performed relatively well on the extensive TREC benchmark.
14153782
SVM Based Learning System for Information Extraction
This paper presents an SVM-based learning system for information extraction (IE). One distinctive feature of our system is the use of a variant of the SVM, the SVM with uneven margins, which is particularly helpful for small training datasets. In addition, our approach needs fewer SVM classifiers to be trained than other recent SVM-based systems. The paper also compares our approach to several state-of-the-art systems (including rule learning and statistical learning algorithms) on three IE benchmark datasets: CoNLL-2003, CMU seminars, and the software jobs corpus. The experimental results show that our system outperforms a recent SVM-based system on CoNLL-2003, achieves the highest score on eight out of 17 categories on the jobs corpus, and is second best on the remaining nine.
11166931
Automatic Extraction of Hierarchical Relations from Text
Automatic extraction of semantic relationships between entity instances in an ontology is useful for attaching richer semantic metadata to documents. In this paper we propose an SVM based approach to hierarchical relation extraction, using features derived automatically from a number of GATE-based open-source language processing tools. In comparison to the previous works, we use several new features including part of speech tag, entity subtype, entity class, entity role, semantic representation of sentence and WordNet synonym set. The impact of the features on the performance is investigated, as is the impact of the relation classification hierarchy. The results show there is a trade-off among these factors for relation extraction and the features containing more information such as semantic ones can improve the performance of the ontological relation extraction task.
10269891
An analytical grasp planning on given object with multifingered hand
In this paper, an analytical approach is proposed for planning finger positions of grasping an object with a multifingered hand. First, a method is given to obtain which combination of the object edges is possible to be used for grasping. Then, a graspable finger position region (GFPR) on a combination of edges is defined where the object can be held successfully. It is shown that the region is bounded by several boundary hyperplanes. By combining these boundary hyperplanes, two propositions for analytically and exactly obtaining the GFPR are proposed. An algorithm is proposed to find a stable GFPR that contains the biggest inscribed hypersphere of GFPR and has the largest volume. Finally, a numerical example is performed to show the effectiveness of the proposed grasp planning approach.
737421
Recognising trajectories of facial identities using kernel discriminant analysis
We present a comprehensive approach to address three challenging problems in face recognition: modelling faces across multi-views, extracting the nonlinear discriminating features, and recognising moving faces dynamically in image sequences. A multi-view dynamic face model is designed to extract the shape-and-pose-free facial texture patterns. Kernel discriminant analysis, which employs the kernel technique to perform linear discriminant analysis in a high-dimensional feature space, is developed to extract the significant nonlinear features which maximise the between-class variance and minimise the within-class variance. Finally, an identity surface based face recognition is performed dynamically from video input by matching object and model trajectories. q 2003 Elsevier B.V. All rights reserved.
7190108
NaLIX: an interactive natural language interface for querying XML
Database query languages can be intimidating to the non-expert, leading to the immense recent popularity for keyword based search in spite of its significant limitations. The holy grail has been the development of a natural language query interface. We present NaLIX, a generic interactive natural language query interface to an XML database. Our system can accept an arbitrary English language sentence as query input, which can include aggregation, nesting, and value joins, among other things. This query is translated, potentially after reformulation, into an XQuery expression that can be evaluated against an XML database. The translation is done through mapping grammatical proximity of natural language parsed tokens to proximity of corresponding elements in the result XML. In this demonstration, we show that NaLIX, while far from being able to pass the Turing test, is perfectly usable in practice, and able to handle even quite complex queries in a variety of application domains. In addition, we also demonstrate how carefully designed features in NaLIX facilitate the interactive query process and improve the usability of the interface.
5262698
An integrated algorithm of incremental and robust PCA
Principal component analysis (PCA) is a well-established technique in image processing and pattern recognition. Incremental PCA and robust PCA are two interesting problems with numerous potential applications. However, these two issues have only been separately addressed in the previous studies. In this paper, we present a novel algorithm for incremental and robust PCA by seamlessly integrating the two issues together. The proposed algorithm has the advantages of both incremental PCA and robust PCA. Moreover, unlike most M-estimation based robust algorithms, it is computational efficient. Experimental results on dynamic background modelling are provided to show the performance of the algorithm with a comparison to the conventional batch-mode and nonrobust algorithms.
18611349
Online Adaptive Fault-Tolerant Routing in 2D Torus
In this paper, we propose efficient routing algorithms for 2D torus with possible large number of faulty nodes. There is no presumption on the number and the distribution of faulty nodes. The proposed algorithms find a fault-free path between any two nonfaulty nodes with high probability in linear time by using only the local routing information of the network. The results of our empirical analysis through simulations show that the algorithms can find a fault-free path between any two nonfaulty nodes with high probability. For example, in a torus of size up to 128×128, where, the number of faulty nodes up to 15%, the heuristuc-square routing algorithm finds a fault-free path with a probability of 90% or higher. The experimental results are impressive for 2D torus with only four links per node.