6770135
Empirical comparison of analog and digital auditory preprocessing for automatic speech recognition
Results from digital and analog filter bank preprocessors are compared in order to establish the validity of analog processing for automatic speech recognition (ASR) systems. Three systems are evaluated using speaker and context independent phoneme recognition tasks. The three ASR systems are identical except for the preprocessing techniques used to derive three signal representations: extraction of (1) the digital mel-frequency spectrum, (2) the mel-frequency spectrum from commercial discrete bandpass filters and (3) the exponential spectrum from analog VLSI bandpass filter bank. The discrete analog system exhibits a 38% increase in recognition accuracy over the digital preprocessing technique. The digital and analog VLSI-based techniques perform comparably (within 3% of each other).
33207616
Semi-parallel rank-order filtering in analog VLSI
This paper presents an implementation of semi-parallel rank-order filtering in analog VLSI to collect position-encoded rank data. The rank competition is based on voltage inputs and a popular winner-take-all (WTA) circuit. The input competition is performed in parallel and each rank is determined serially. Negative feedback ensures that neither the previous winner nor its nearest neighbors will compete in the new rank competition. This circuit is compact (21 transistors per input element), and can serve unlimited number of input elements. The 20 element array that we have fabricated and tested consumes approximately 0.2 mW of power, has good resolution (2 mV between inputs) and good response time (0.05 ms) in subthreshold operation.
6823058
DubLet: An Online CBR System for Rental Property Recommendation
Searching for accommodation in Dublin's fast-moving rental market can be a difficult and frustrating process. Ideally, the accommodation seeker would have access to a support system that could consolidate available rental information, recommend suitable properties, and always be at hand with the most current information. This paper describes the fully implemented DubLet system to provide just such support. DubLet is an application that employs core CBR methods to recommend rental properties drawn from a number of online sources using Information Extraction techniques. User interaction is provided both via web-browser and via WAP mobile phone. The paper gives an overview of the DubLet system implementation, presents an evaluation of the extraction and recommendation methods, and describes future system directions.
56631463
Explicit vs Implicit Profiling - A Case-Study in Electronic Programme Guides
In this paper, we evaluate the use of implicit interest indicators as the basis for user profiling in the Digital TV domain. Research in more traditional domains, such as Web browsing or Usenet News, indicates that some implicit interest indicators (e.g., read-time and mouse movements) are capable of serving as alternative to explicit profile information such as user ratings. Consequently, the key question we wish to answer relates to the type of implicit indicators that can be identified within the DTV domain and the extent to which they can accurately reflect a user's true preferences.
17761979
New Normalization Methods for CDNA Microarray Data
MOTIVATION
The focus of this paper is on two new normalization methods for cDNA microarrays. After the image analysis has been performed on a microarray and before differentially expressed genes can be detected, some form of normalization must be applied to the microarrays. Normalization removes biases towards one or other of the fluorescent dyes used to label each mRNA sample allowing for proper evaluation of differential gene expression.


RESULTS
The two normalization methods that we present here build on previously described non-linear normalization techniques. We extend these techniques by firstly introducing a normalization method that deals with smooth spatial trends in intensity across microarrays, an important issue that must be dealt with. Secondly we deal with normalization of a new type of cDNA microarray experiment that is coming into prevalence, the small scale specialty or 'boutique' array, where large proportions of the genes on the microarrays are expected to be highly differentially expressed.


AVAILABILITY
The normalization methods described in this paper are available via http://www.pi.csiro.au/gena/ in a software suite called tRMA: tools for R Microarray Analysis upon request of the authors. Images and data used in this paper are also available via the same link.
5565732
Instance Pruning Techniques
The nearest neighbor algorithm and its derivatives are often quite successful at learning a concept from a training set and providing good generalization on subsequent input vectors. However, these techniques often retain the entire training set in memory, resulting in large memory requirements and slow execution speed, as well as a sensitivity to noise. This paper provides a discussion of issues related to reducing the number of instances retained in memory while maintaining (and sometimes improving) generalization accuracy, and mentions algorithms other researchers have used to address this problem. It presents three intuitive noise-tolerant algorithms that can be used to prune instances from the training set. In experiments on 29 applications, the algorithm that achieves the highest reduction in storage also results in the highest generalization accuracy of the three methods.
5596125
Preserving recommender accuracy and diversity in sparse datasets
Recommender systems combine research from user profiling, information filtering and artificial intelligence to provide users with more intelligent information access. They have proven to be useful in a range of Internet and e-commerce applications. Recent research has shown that a content-based (or case-based) perspective on collaborative filtering for recommendation can provide significant benefits in decision support accuracy over traditional collaborative techniques, particularly as dataset sparsity increases. These benefits derive both from the use of more sophisticated case-based similarity metrics and from the proactive maintenance of item similarity knowledge using data mining. This article presents a natural next step in this ongoing research to improve the quality of recommender systems by validating these findings in the context of more complex models of collaborative filtering, as well as by demonstrating that such techniques also preserve recommendation diversity, one of the key issues affecting traditional recommender systems.