Query: Wavelet-based statistical signal processing using hidden Markov models

Relevant passages:
– “ Mixture Densities , Maximum Likelihood and the EM Algorithm <=SEP=> The problem of estimating the parameters which determine a mixture density has been the subject of a large, diverse body of literature spanning nearly ninety years. During the last two decades, the method of maximum likelihood has become the most widely followed approach to this problem, thanks primarily to the advent of high speed electronic computers. Here, we first offer a brief survey of the literature directed toward this problem and review maximum-likelihood estimation for it. We then turn to the subject of ultimate interest, which is a particular iterative procedure for numerically approximating maximum-likelihood estimates for mixture density problems. This procedure, known as the EM algorithm, is a specialization to the mixture density context of a general algorithm of the same name used to approximate maximum-likelihood estimates for incomplete data problems. We discuss the formulation and theoretical and practical properties of the EM algorithm for mixture densities, focussing in particular on mixtures of densities from exponential families.

A multiscale random field model for Bayesian image segmentation <=SEP=> Many approaches to Bayesian image segmentation have used maximum a posteriori (MAP) estimation in conjunction with Markov random fields (MRF). Although this approach performs well, it has a number of disadvantages. In particular, exact MAP estimates cannot be computed, approximate MAP estimates are computationally expensive to compute, and unsupervised parameter estimation of the MRF is difficult. The authors propose a new approach to Bayesian image segmentation that directly addresses these problems. The new method replaces the MRF model with a novel multiscale random field (MSRF) and replaces the MAP estimator with a sequential MAP (SMAP) estimator derived from a novel estimation criteria. Together, the proposed estimator and model result in a segmentation algorithm that is not iterative and can be computed in time proportional to MN where M is the number of classes and N is the number of pixels. The also develop a computationally efficient method for unsupervised estimation of model parameters. Simulations on synthetic images indicate that the new algorithm performs better and requires much less computation than MAP estimation using simulated annealing. The algorithm is also found to improve classification accuracy when applied to the segmentation of multispectral remotely sensed images with ground truth data.

Singularity detection and processing with wavelets <=SEP=> Most of a signal information is often carried by irregular structures and transient phenomena. The mathematical characterization of singularities with Lipschitz exponents is explained. Theorems are reviewed that estimate local Lipschitz exponents of functions from the evolution across scales of their wavelet transform. It is then proven that the local maxima of the wavelet transform modulus detect the locations of irregular structures and provide numerical procedures to compute their Lipschitz exponents. The wavelet transform of singularities with fast oscillations have a particular behavior that is studied separately. The local frequency of such oscillations are measured from the wavelet transform modulus maxima. It has been shown numerically that oneand two-dimensional signals can be reconstructed, with a good approximation, from the local maxima of their wavelet transform modulus. As an application, an algorithm is developed that removes white noises from signals by analyzing the evolution of the wavelet transform maxima across scales. In two-dimensions, the wavelet transform maxima indicate the location of edges in images. The denoising algorithm is extended for image enhancement.

Embedded image coding using zerotrees of wavelet coefficients <=SEP=> The embedded zerotree wavelet algorithm (EZW) is a simple, yet remarkably effective, image compression algorithm, having the property that the bits in the bit stream are generated in order of importance, yielding a fully embedded code. The embedded code represents a sequence of binary decisions that distinguish a n image from the “null” image. Using a n embedded coding algorithm, a n encoder can terminate the encoding a t any point thereby allowing a target rate or target distortion metric to be met exactly. Also, given a bit stream, the decoder can cease decoding a t any point in the bit stream and still produce exactly the same image that would have been encoded a t the bit rate corresponding to the truncated bit stream. In addition to producing a fully embedded bit stream, EZW consistently produces compression results that a re competitive with virtually all known compression algorithms on standard test images. Yet this performance is achieved with a technique that requires absolutely no training, no pre-stored tables or codebooks, and requires no prior knowledge of the image source. The EZW algorithm is based on four key concepts: 1) a discrete wavelet transform or hierarchical subband decomposition, 2) prediction of the absence of significant information across scales by exploiting the self-similarity inherent in images, 3) entropy-coded successive-approximation quantization, and 4) universal lossless data compression which is achieved via adaptive arithmetic coding.

A tutorial on hidden Markov models and selected applications in speech recognition <=SEP=> 

A 600MS/s 30mW 0.13µm CMOS ADC array achieving over 60dB SFDR with adaptive digital equalization <=SEP=> At high conversion speed, time interleaving provides a viable way of achieving analog-to-digital conversion with low power consumption, especially when combined with the successive-approximation-register (SAR) architecture that is known to scale well in CMOS technology. In this work, we showcase a digital background-equalization technique to treat the path-mismatch problem as well as individual ADC nonlinearities in time-interleaved SAR ADC arrays. This approach was first introduced to calibrate the linearity errors in pipelined ADCs [1], and subsequently extended to treat SAR ADCs [2]. In this prototype, we demonstrate the effectiveness of this technique in a compact SAR ADC array, which achieves 7.5 ENOB and a 65dB SFDR at 600MS/s while dissipating 23.6mW excluding the on-chip DLL, and exhibiting one of the best conversion FOMs among ADCs with similar sample rates and resolutions [3].

LateralPaD: A surface-haptic device that produces lateral forces on a bare finger <=SEP=> The LateralPaD is a surface haptic device that generates lateral (shear) force on a bare finger, by vibrating the touch surface simultaneously in both out-of-plane (normal) and in-plane (lateral) directions. A prototype LateralPaD has been developed in which the touch surface is glass, and piezoelectric actuators drive normal and lateral resonances at the same ultrasonic frequency (~22.3 KHz). The force that develops on the finger can be controlled by modulating the relative phase of the two resonances. A 2DOF load cell setup is used to characterize the dependence of induced lateral force on vibration amplitude, relative phase, and applied normal force. A Laser Doppler Vibrometer (LDV) is used to measure the motion of glass surface as well as of the fingertip. Together, these measurements yield insight into the mechanism of lateral force generation. We show evidence for a mechanism dependent on tilted impacts between the LateralPaD and fingertip.

A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution <=SEP=> We address the problem of image upscaling in the form of single image super-resolution based on a dictionary of lowand highresolution exemplars. Two recently proposed methods, Anchored Neighborhood Regression (ANR) and Simple Functions (SF), provide state-ofthe-art quality performance. Moreover, ANR is among the fastest known super-resolution methods. ANR learns sparse dictionaries and regressors anchored to the dictionary atoms. SF relies on clusters and corresponding learned functions. We propose A+, an improved variant of ANR, which combines the best qualities of ANR and SF. A+ builds on the features and anchored regressors from ANR but instead of learning the regressors on the dictionary it uses the full training material, similar to SF. We validate our method on standard images and compare with state-of-the-art methods. We obtain improved quality (i.e. 0.2-0.7dB PSNR better than ANR) and excellent time complexity, rendering A+ the most efficient dictionary-based super-resolution method to date.

Experience Prototyping <=SEP=> In this paper, we describe "Experience Prototyping" as a form of prototyping that enables design team members, users and clients to gain first-hand appreciation of existing or future conditions through active engagement with prototypes. We use examples from commercial design projects to illustrate the value of such prototypes in three critical design activities: understanding existing experiences, exploring design ideas and in communicating design concepts.

Supernumerary nostril: a case report <=SEP=> BACKGROUND
Supernumerary nostril is a congenital anomaly that contains additional nostril with or without accessory cartilage. These rare congenital nasal deformities result from embryological defects. Since 1906, Lindsay (Trans Pathol Soc Lond. 57:329-330, 1906) has published the first research of bilateral supernumerary nostrils, and only 34 cases have been reported so far in the English literature.


CASE PRESENTATION
A 1-year-old female baby was brought to our department group for the treatment of an accessory opening above the left nostril which had been presented since her birth. Medical history was non-specific and her birth was normal. The size of a supernumerary nostril was about 0.2 cm diameter and connected to the left nostril. The right one was normal. Minimal procedure was operated for the anomaly. After 1 year, rhinoplasty was performed for the nostril asymmetry.


CONCLUSIONS
At 1 year follow-up, the functional and cosmetic result was satisfactory. In this case, it is important that we have early preoperative diagnosis. Also, it is desirable that we should perform a corrective surgery as soon as possible for the patient's psychosocial growth.

ADvanced IMage Algebra (ADIMA): a novel method for depicting multiple
sclerosis lesion heterogeneity, as demonstrated by quantitative MRI <=SEP=> BACKGROUND
There are modest correlations between multiple sclerosis (MS) disability and white matter lesion (WML) volumes, as measured by T2-weighted (T2w) magnetic resonance imaging (MRI) scans (T2-WML). This may partly reflect pathological heterogeneity in WMLs, which is not apparent on T2w scans.


OBJECTIVE
To determine if ADvanced IMage Algebra (ADIMA), a novel MRI post-processing method, can reveal WML heterogeneity from proton-density weighted (PDw) and T2w images.


METHODS
We obtained conventional PDw and T2w images from 10 patients with relapsing-remitting MS (RRMS) and ADIMA images were calculated from these. We classified all WML into bright (ADIMA-b) and dark (ADIMA-d) sub-regions, which were segmented. We obtained conventional T2-WML and T1-WML volumes for comparison, as well as the following quantitative magnetic resonance parameters: magnetisation transfer ratio (MTR), T1 and T2. Also, we assessed the reproducibility of the segmentation for ADIMA-b, ADIMA-d and T2-WML.


RESULTS
Our study's ADIMA-derived volumes correlated with conventional lesion volumes (p < 0.05). ADIMA-b exhibited higher T1 and T2, and lower MTR than the T2-WML (p < 0.001). Despite the similarity in T1 values between ADIMA-b and T1-WML, these regions were only partly overlapping with each other. ADIMA-d exhibited quantitative characteristics similar to T2-WML; however, they were only partly overlapping. Mean intra- and inter-observer coefficients of variation for ADIMA-b, ADIMA-d and T2-WML volumes were all < 6 % and < 10 %, respectively.


CONCLUSION
ADIMA enabled the simple classification of WML into two groups having different quantitative magnetic resonance properties, which can be reproducibly distinguished.

Hybrid speech recognition with Deep Bidirectional LSTM <=SEP=> Deep Bidirectional LSTM (DBLSTM) recurrent neural networks have recently been shown to give state-of-the-art performance on the TIMIT speech database. However, the results in that work relied on recurrent-neural-network-specific objective functions, which are difficult to integrate with existing large vocabulary speech recognition systems. This paper investigates the use of DBLSTM as an acoustic model in a standard neural network-HMM hybrid system. We find that a DBLSTM-HMM hybrid gives equally good results on TIMIT as the previous work. It also outperforms both GMM and deep network benchmarks on a subset of the Wall Street Journal corpus. However the improvement in word error rate over the deep network is modest, despite a great increase in framelevel accuracy. We conclude that the hybrid approach with DBLSTM appears to be well suited for tasks where acoustic modelling predominates. Further investigation needs to be conducted to understand how to better leverage the improvements in frame-level accuracy towards better word error rates.

A Survey of Logical Models for OLAP Databases <=SEP=> In this paper, we present different proposals for multidimensional data cubes, which are the basic logical model for OLAP applications. We have grouped the work in the field in two categories: commercial tools (presented along with terminology and standards) and academic efforts. We further divide the academic efforts in two subcategories: the relational model extensions and the cube-oriented approaches. Finally, we attempt a comparative analysis of the various efforts.

Design of Multiple-Level Hybrid Classifier for Intrusion Detection System <=SEP=> As the number of networked computers grows, intrusion detection is an essential component in keeping networks secure. However, constructing and maintaining a misuse detection system is very labor-intensive since attack scenarios and patterns need to be analyzed and categorized, and the corresponding rules and patterns need to be carefully hand-coded. Thus, data mining can be used to ease this inconvenience. This paper proposes a multiple-level hybrid classifier, an intrusion detection system that uses a combination of tree classifiers and clustering algorithms to detect intrusions. Performance of this new algorithm is compared to other popular approaches such as MADAM ID and 3-level tree classifiers, and significant improvement has been achieved from the viewpoint of both high intrusion detection rate and reasonably low false alarm rate

Project Lachesis: Parsing and Modeling Location Histories <=SEP=> A datatype with increasing importance in GIS is what we call the location history–a record of an entity’s location in geographical space over an interval of time. This paper proposes a number of rigorously defined data structures and algorithms for analyzing and generating location histories. Stays are instances where a subject has spent some time at a single location, and destinations are clusters of stays. Using stays and destinations, we then propose two methods for modeling location histories probabilistically. Experiments show the value of these data structures, as well as the possible applications of probabilistic models of location histories.

Extending feature usage: a study of the post-adoption of electronic medical records <=SEP=> In order to study the factors that influence how professionals use complex systems, we create a tentative research model that builds on PAM and TAM. Specifically we include PEOU and the construct ‘Professional Association Guidance’. We postulate that feature usage is enhanced when professional associations influence PU by highlighting additional benefits. We explore the theory in the context of post-adoption use of Electronic Medical Records (EMRs) by primary care physicians in Ontario. The methodology can be extended to other professional environments and we suggest directions for future research.

Information theory-based shot cut/fade detection and video summarization <=SEP=> New methods for detecting shot boundaries in video sequences and for extracting key frames using metrics based on information theory are proposed. The method for shot boundary detection relies on the mutual information (MI) and the joint entropy (JE) between the frames. It can detect cuts, fade-ins and fade-outs. The detection technique was tested on the TRECVID2003 video test set having different types of shots and containing significant object and camera motion inside the shots. It is demonstrated that the method detects both fades and abrupt cuts with high accuracy. The information theory measure provides us with better results because it exploits the inter-frame information in a more compact way than frame subtraction. It was also successfully compared to other methods published in literature. The method for key frame extraction uses MI as well. We show that it captures satisfactorily the visual content of the shot.

Small Discrete Fourier Transforms on GPUs <=SEP=> Efficient implementations of the Discrete Fourier Transform (DFT) for GPUs provide good performance with large data sizes, but are not competitive with CPU code for small data sizes. On the other hand, several applications perform multiple DFTs on small data sizes. In fact, even algorithms for large data sizes use a divide-and-conquer approach, where eventually small DFTs need to be performed. We discuss our DFT implementation, which is efficient for multiple small DFTs. One feature of our implementation is the use of the asymptotically slow matrix multiplication approach for small data sizes, which improves performance on the GPU due to its regular memory access and computational patterns. We combine this algorithm with the mixed radix algorithm for 1-D, 2-D, and 3-D complex DFTs. We also demonstrate the effect of different optimization techniques. When GPUs are used to accelerate a component of an application running on the host, it is important that decisions taken to optimize the GPU performance not affect the performance of the rest of the application on the host. One feature of our implementation is that we use a data layout that is not optimal for the GPU so that the overall effect on the application is better. Our implementation performs up to two orders of magnitude faster than cuFFT on an NVIDIA GeForce 9800 GTX GPU and up to one to two orders of magnitude faster than FFTW on a CPU for multiple small DFTs. Furthermore, we show that our implementation can accelerate the performance of a Quantum Monte Carlo application for which cuFFT is not effective. The primary contributions of this work lie in demonstrating the utility of the matrix multiplication approach and also in providing an implementation that is efficient for small DFTs when a GPU is used to accelerate an application running on the host.

Groundnut leaf disease detection and classification by using back probagation algorithm <=SEP=> Many studies shows that quality of agricultural products may be reduced from many causes. One of the most important factors contributing to low yield is disease attack. The plant disease such as fungi, bacteria and viruses. The leaf disease completely destroys the quality of the leaf. Common ground nut disease is cercospora. It is one of the type of disease in early stage of ground nut leaf. The upgraded processing pattern comprises of four leading steps. Initially a color renovation constitute intended for the input RGB image is formed, This RGB is converted into HSV because RGB is for Color generation and color descriptor. The next step is plane separation. Next performed the color features. Then using back propagation algorithm detection of leaf disease is done.

Development of Robotic Gloves for Hand Rehabilitation Post-Stroke <=SEP=> This paper presents the hardware and software developments that have been achieved and implemented in the robotic gloves for hand rehabilitation post-stroke. The practical results are shown that prove the functionalities of the robotic gloves in common operating conditions. This work focused on development of a lightweight and low-cost robotic glove that patients can wear and use to recover hand functionality. We developed some structures for the robotic glove, as exoskeleton and as soft robotic glove. The paper presents a comparison of these structures.

Customised Visualisations of Linked Open Data <=SEP=> This paper aims to tackle on Linked Open Data (LOD) customised visualisations. The work is part of an ongoing research on interfaces and tools for helping non-programmers to easily present, analyse and support sense making over large semantic dataset. The customisation is a key aspect of our work. Producing effective customised visualisations is still difficult due to the complexity of the existing tools and the limited set of options they offer, especially to those with little knowledge in LOD and semantic data models. How can we give users full control on the primitives of the visualisation and their properties, without requiring them to master Semantic Web technologies or programming languages? The paper presents a conceptual model that can be used as a reference to build tools for generating customisable infoviews. We used it to conduct a survey on existing tools in terms of customisation capabilities. Starting from the feedback collected in this phase, we will implement these customisation features into some prototypes we are currently experimenting on.

Enhancing representation learning with tensor decompositions for knowledge graphs and high dimensional sequence modeling <=SEP=> The capability of processing and digesting raw data is one of the key features of a humanlike artificial intelligence system. For instance, real-time machine translation should be able to process and understand spoken natural language, and autonomous driving relies on the comprehension of visual inputs. Representation learning is a class of machine learning techniques that autonomously learn to derive latent features from raw data. These new features are expected to represent the data instances in a vector space that facilitates the machine learning task. This thesis studies two specific data situations that require efficient representation learning: knowledge graph data and high dimensional sequences. In the first part of this thesis, we first review multiple relational learning models based on tensor decomposition for knowledge graphs. We point out that relational learning is in fact a means of learning representations through one-hot mapping of entities. Furthermore, we generalize this mapping function to consume a feature vector that encodes all known facts about each entity. It enables the relational model to derive the latent representation instantly for a new entity, without having to re-train the tensor decomposition. In the second part, we focus on learning representations from high dimensional sequential data. Sequential data often pose the challenge that they are of variable lengths. Electronic health records, for instance, could consist of clinical event data that have been collected at subsequent time steps. But each patient may have a medical history of variable length. We apply recurrent neural networks to produce fixed-size latent representations from the raw feature sequences of various lengths. By exposing a prediction model to these learned representations instead of the raw features, we can predict the therapy prescriptions more accurately as a means of clinical decision support. We further propose Tensor-Train recurrent neural networks. We give a detailed introduction to the technique of tensorizing and decomposing large weight matrices into a few smaller tensors. We demonstrate the specific algorithms to perform the forward-pass and the back-propagation in this setting. Then we apply this approach to the input-to-hidden weight matrix in recurrent neural networks. This novel architecture can process extremely high dimensional sequential features such as video data. The model also provides a promising solution to processing sequential features with high sparsity. This is, for instance, the case with electronic health records, since they are often of categorical nature and have to be binary-coded. We incorporate a statistical survival model with this representation learning model, which shows superior prediction quality.

Analyzing and Managing Role-Based Access Control Policies <=SEP=> Today more and more security-relevant data is stored on computer systems; security-critical business processes are mapped to their digital counterparts. This situation applies to various domains such as health care industry, digital government, and financial service institutes requiring that different security requirements must be fulfilled. Authorisation constraints can help the policy architect design and express higher-level organisational rules. Although the importance of authorisation constraints has been addressed in the literature, there does not exist a systematic way to verify and validate authorisation constraints. In this paper, we specify both non-temporal and history-based authorisation constraints in the Object Constraint Language (OCL) and first-order linear temporal logic (LTL). Based upon these specifications, we attempt to formally verify role-based access control policies with the help of a theorem prover and to validate policies with the USE system, a validation tool for OCL constraints. We also describe an authorisation engine, which supports the enforcement of authorisation constraints.

Greedy Layerwise Learning Can Scale to ImageNet <=SEP=> Shallow supervised 1-hidden layer neural networks have a number of favorable properties that make them easier to interpret, analyze, and optimize than their deep counterparts, but lack their representational power. Here we use 1-hidden layer learning problems to sequentially build deep networks layer by layer, which can inherit properties from shallow networks. Contrary to previous approaches using shallow networks, we focus on problems where deep learning is reported as critical for success. We thus study CNNs on image classification tasks using the large-scale ImageNet dataset and the CIFAR-10 dataset. Using a simple set of ideas for architecture and training we find that solving sequential 1-hidden-layer auxiliary problems lead to a CNN that exceeds AlexNet performance on ImageNet. Extending this training methodology to construct individual layers by solving 2-and-3-hidden layer auxiliary problems, we obtain an 11-layer network that exceeds several members of the VGG model family on ImageNet, and can train a VGG-11 model to the same accuracy as end-to-end learning. To our knowledge, this is the first competitive alternative to end-to-end training of CNNs that can scale to ImageNet. We illustrate several interesting properties of these models theoretically and conduct a range of experiments to study the properties this training induces on the intermediate representations.

Rethinking the Value of Network Pruning <=SEP=> Network pruning is widely used for reducing the heavy computational cost of 1 deep models. A typical pruning algorithm is a three-stage pipeline, i.e., training (a 2 large model), pruning and fine-tuning. In this work, we make a rather surprising 3 observation: fine-tuning a pruned model only gives comparable or even worse 4 performance than training that model with randomly initialized weights. Our 5 results have several implications: 1) training a large, over-parameterized model is 6 not necessary to obtain an efficient final model, 2) learned “important” weights 7 of the large model are not necessarily useful for the small pruned model, 3) the 8 pruned architecture itself, rather than a set of inherited weights, is what leads to the 9 efficiency benefit in the final model, which suggests that some pruning algorithms 10 could be seen as performing network architecture search. 11

Compact wideband directional antenna with three-dimensional structure for microwave-based head imaging systems <=SEP=> Microwave-based head imaging systems demand wideband antennas with compact size. To ensure high scattered signals from the intended target point, the antennas need to have directional radiation patterns. Designing an antenna by addressing all these issues is challenging as the head imaging system utilizes low microwave frequencies (around 1-3 GHz) to maintain high signal penetration in the lossy human head. This paper reports a coplanar waveguide-fed design that overcomes all these limitations. The antenna is constructed in a three-dimensional manner by combining a dipole antenna with a folded parasitic structure printed on two substrate blocks. The antenna mechanism is explained with the description of its design parameters. The antenna covers 101% fractional bandwidth with a stable gain, good radiation efficiency and well directional radiation characteristics. The overall volume of the antenna is 0.24 × 0.1 × 0.05 λm where λm is the wavelength at the lowest frequency of operation.

A spiral antenna over a high-impedance surface consisting of fan-shaped patch cells <=SEP=> Radiation characteristics of a spiral antenna over a high-impedance surface (HIS) are analyzed. The HIS consists of fan-shaped patch cells. The fan-shaped patches are arranged homogeneously in the circumferential direction but are set non-homogeneously in the radial direction. The analysis is performed using method of moment. Radiation characteristics of a spiral antenna with a perfect electric conductor (PEC) reflector are analyzed. It is reaffirmed that wideband radiation characteristics, including input impedance and axial ratio are deteriorate. Subsequently, Radiation characteristics of a spiral antenna with a fan-shaped HIS reflector are analyzed. It is revealed that input impedance and axial ratio are mitigated by replacing the PEC reflector with the fan-shaped HIS reflector.

Autoencoders, Unsupervised Learning, and Deep Architectures <=SEP=> To better understand deep architectures and unsupervised learning, uncluttered by hardware details, we develop a general autoencoder framework for the comparative study of autoencoders, including Boolean autoencoders. We derive several results regarding autoencoders and autoencoder learning, including results on learning complexity, vertical and horizontal composition, and fundamental connections between critical points and clustering. Possible implications for the theory of deep architectures are discussed.

Accelerating Differential Evolution Using an Adaptive Local Search <=SEP=> We propose a crossover-based adaptive local search (LS) operation for enhancing the performance of standard differential evolution (DE) algorithm. Incorporating LS heuristics is often very useful in designing an effective evolutionary algorithm for global optimization. However, determining a single LS length that can serve for a wide range of problems is a critical issue. We present a LS technique to solve this problem by adaptively adjusting the length of the search, using a hill-climbing heuristic. The emphasis of this paper is to demonstrate how this LS scheme can improve the performance of DE. Experimenting with a wide range of benchmark functions, we show that the proposed new version of DE, with the adaptive LS, performs better, or at least comparably, to classic DE algorithm. Performance comparisons with other LS heuristics and with some other well-known evolutionary algorithms from literature are also presented.

Logistic Regression and Collaborative Filtering for Sponsored Search Term Recommendation <=SEP=> Sponsored search advertising is largely based on bidding on individual terms. The richness of natural languages permits web searchers to express their information needs in myriad ways. Advertisers have difficulty discovering all the terms that are relevant to their products or services. We examine the performance of logistic regression and collaborative filtering models on two different data sources to predict terms relevant to a set of seed terms describing an advertiser’s product or service.