18641790
Supervised Semi-definite Embedding for Email Data Cleaning and Visualization
The Email systems are playing an important and irreplaceable role in the digital world due to its convenience, efficiency and the rapid growth of World Wide Web (WWW). However, most of the email users nowadays are suffering from the large amounts of irrelevant and noisy emails everyday. Thus algorithms which can clean both the noise features and the irrelevant emails are highly desired. In this paper, we propose a novel Supervised Semi-definite Embedding (SSDE) algorithm to reduce the dimension of email data so as to leave out the noise features of them and visualize these emails in a supervised manner to find the irrelevant ones intuitively. Experiments on a set of received emails of several volunteers during a period of time and some benchmark datasets show the comparable performance of the proposed SSDE algorithm.
1876424
A Similarity Reinforcement Algorithm for Heterogeneous Web Pages
Many machine learning and data mining algorithms crucially rely on the similarity metrics. However, most early research works such as Vector Space Model or Latent Semantic Index only used single relationship to measure the similarity of data objects. In this paper, we first use an Intra- and Inter- Type Relationship Matrix (IITRM) to represent a set of heterogeneous data objects and their inter-relationships. Then, we propose a novel similarity-calculating algorithm over the Inter- and Intra- Type Relationship Matrix. It tries to integrate information from heterogeneous sources to serve their purposes by iteratively computing. This algorithm can help detect latent relationships among heterogeneous data objects. Our new algorithm is based on the intuition that the intra-relationship should affect the inter-relationship, and vice versa. Experimental results on the MSN logs dataset show that our algorithm outperforms the traditional Cosine similarity.
1777247
Web-page classification through summarization
Web-page classification is much more difficult than pure-text classification due to a large variety of noisy information embedded in Web pages. In this paper, we propose a new Web-page classification algorithm based on Web summarization for improving the accuracy. We first give empirical evidence that ideal Web-page summaries generated by human editors can indeed improve the performance of Web-page classification algorithms. We then propose a new Web summarization-based classification algorithm and evaluate it along with several other state-of-the-art text summarization algorithms on the LookSmart Web directory. Experimental results show that our proposed summarization-based classification algorithm achieves an approximately 8.8% improvement as compared to pure-text-based classification algorithm. We further introduce an ensemble classifier using the improved summarization algorithm and show that it achieves about 12.9% improvement over pure-text based methods.
7915358
A novel clustering-based RSS aggregator
In recent years, different commercial Weblog subscribing systems have been proposed to return stories from users. subscribed feeds. In this paper, we propose a novel clustering-based RSS aggregator called as RSS Clusgator System (RCS) for Weblog reading. Note that an RSS feed may have several different topics. A user may only be interested in a subset of these topics. In addition there could be many different stories from multiple RSS feeds, which discuss similar topic from different perspectives. A user may be interested in this topic but do not know how to collect all feeds related to this topic. In contrast to many previous works, we cluster all stories in RSS feeds into hierarchical structure to better serve the readers. Through this way, users can easily find all their interested stories. To make the system current, we propose a flexible time window for incremental clustering. RCS utilizes both link information and content information for efficient clustering. Experiments show the effectiveness of RCS.
32935353
Online Supervised Learning for Digital Library
We propose an online learning algorithm for digital library. It learns from a data stream and overcomes the inherent problem of other incremental operations. Experiments on RCV1 show the superior performance of it.
2174685
Privacy-enhancing personalized web search
Personalized web search is a promising way to improve search quality by customizing search results for people with individual information goals. However, users are uncomfortable with exposing private preference information to search engines. On the other hand, privacy is not absolute, and often can be compromised if there is a gain in service or profitability to the user. Thus, a balance must be struck between search quality and privacy protection. This paper presents a scalable way for users to automatically build rich user profiles. These profiles summarize a user.s interests into a hierarchical organization according to specific interests. Two parameters for specifying privacy requirements are proposed to help the user to choose the content and degree of detail of the profile information that is exposed to the search engine. Experiments showed that the user profile improved search quality when compared to standard MSN rankings. More importantly, results verified our hypothesis that a significant improvement on search quality can be achieved by only sharing some higher-level user profile information, which is potentially less sensitive than detailed personal information.
8056566
Bayesian Evolutionary Optimization Using Helmholtz Machines
Recently, several evolutionary algorithms have been proposed that build and use an explicit distribution model of the population to perform optimization. One of the main issues in this class of algorithms is how to estimate the distribution of selected samples. In this paper, we present a Bayesian evolutionary algorithm (BEA) that learns the sample distribution by a probabilistic graphical model known as Helmholtz machines. Due to the generative nature and availability of the wake-sleep learning algorithm, the Helmholtz machines provide an effective tool for modeling and sampling from the distribution of selected individuals. The proposed method has been applied to a suite of GA-deceptive functions. Experimental results show that the BEA with the Helmholtz machine outperforms the simple genetic algorithm.
6883417
Fast k-nearest neighbor classification using cluster-based trees
Most fast k-nearest neighbor (k-NN) algorithms exploit metric properties of distance measures for reducing computation cost and a few can work effectively on both metric and nonmetric measures. We propose a cluster-based tree algorithm to accelerate k-NN classification without any presuppositions about the metric form and properties of a dissimilarity measure. A mechanism of early decision making and minimal side-operations for choosing searching paths largely contribute to the efficiency of the algorithm. The algorithm is evaluated through extensive experiments over standard NIST and MNIST databases.
37126329
Genetic programming of process decomposition strategies for evolvable hardware
Evolvable hardware is able to offer considerably higher performance than general-purpose processors and significantly more flexibility than ASICs. In order to take the advantages of general-purpose processors and ASICs, dividing a complex process into subprocesses is essential. In this paper, we propose a evolutionary method called context switching that splits a task into a set of subtasks whose complexity is manageable on the given hardware. The method is based on genetic programming. Due to its expressive power generic program can represent flexible strategies for decomposing complex tasks. The effectiveness of context switching is demonstrated on the design of adaptive controllers for a team of autonomous mobile robots.
986422
Individuality of numerals
The analysis of handwritten documents from the view-pointof determining their writership has great bearing onthe criminal justice system. In many cases, only a limitedamount of handwriting is available and sometimes it consistsof only numerals. Using a large number of handwrittennumeral images extracted from about 3000 samples writtenby 1000 writers, a study of the individuality of numerals foridentification/verification purposes was conducted. The individualityof numerals was studied using cluster analysis.Numerals discriminability was measured for writer verification.The study shows that some numerals present a higherdiscriminatory power and that their performances for theverification/identification tasks are very different.
14677925
Universal IP multicast delivery
A ubiquitous and efficient multicast data delivery service is essential to the success of large-scale group communication applications. The original IP multicast design is to enhance network routers with multicast capability [S. Deering, D. Cheriton, Multicast routing in datagram internetworks and extended LANs, ACM Transactions on Computer Systems 8(2) (1990) 85-110]. This approach can achieve great transmission efficiency and performance but also poses a critical dependency on universal deployment. A different approach, overlay multicast, moves multicast functionality to end hosts, thereby removing the dependency on router deployment, albeit at the cost of noticeable performance penalty compared to IP multicast. In this paper we present the Universal Multicast (UM) framework, along with a set of mechanisms and protocols, to provide ubiquitous multicast delivery service on the Internet. Our design can fully utilize native IP multicast wherever it is available, and automatically build unicast tunnels to connect IP Multicast "islands" to form an overall multicast overlay. The UM design consists of three major components: an overlay multicast protocol (HMTP) for inter-island routing, an intra-island multicast management protocol (HGMP) to glue overlay multicast and native IP multicast together, and a daemon program to implement the functionality at hosts. In addition to performance evaluation through simulations, we have also implemented parts of the UM framework. Our prototype implementation has been used to broadcast several workshops and the ACM SIGCOMM 2004 conference live on the Internet. We present some statistics collected during the live broadcast and describe mechanisms we adopted to support end hosts behind Network Address Translation (NAT) gateways and firewalls.