229019
A Machine Learning Approach to TCP Throughput Prediction
TCP throughput prediction is an important capability for networks where multiple paths exist between data senders and receivers. In this paper, we describe a new lightweight method for TCP throughput prediction. Our predictor uses Support Vector Regression (SVR); prediction is based on both prior file transfer history and measurements of simple path properties. We evaluate our predictor in a laboratory setting where ground truth can be measured with perfect accuracy. We report the performance of our predictor for oracular and practical measurements of path properties over a wide range of traffic conditions and transfer sizes. For bulk transfers in heavy traffic using oracular measurements, TCP throughput is predicted within 10% of the actual value 87% of the time, representing nearly a threefold improvement in accuracy over prior history-based methods. For practical measurements of path properties, predictions can be made within 10% of the actual value nearly 50% of the time, approximately a 60% improvement over history-based methods, and with much lower measurement traffic overhead. We implement our predictor in a tool called PathPerf, test it in the wide area, and show that PathPerf predicts TCP throughput accurately over diverse wide area paths.
6597740
Semi-supervised classification with hybrid generative/discriminative methods
We compare two recently proposed frameworks for combining generative and discriminative probabilistic classifiers and apply them to semi-supervised classification. In both cases we explore the tradeoff between maximizing a discriminative likelihood of labeled data and a generative likelihood of labeled and unlabeled data. While prominent semi-supervised learning methods assume low density regions between classes or are subject to generative modeling assumptions, we conjecture that hybrid generative/discriminative methods allow semi-supervised learning in the presence of strongly overlapping classes and reduce the risk of modeling structure in the unlabeled data that is irrelevant for the specific classification task of interest. We apply both hybrid approaches within naively structured Markov random field models and provide a thorough empirical comparison with two well-known semi-supervised learning methods on six text classification tasks. A semi-supervised hybrid generative/discriminative method provides the best accuracy in 75% of the experiments, and the multi-conditional learning hybrid approach achieves the highest overall mean accuracy across all tasks.
2283419
Nonparametric Transforms of Graph Kernels for Semi-Supervised Learning
We present an algorithm based on convex optimization for constructing kernels for semi-supervised learning. The kernel matrices are derived from the spectral decomposition of graph Laplacians, and combine labeled and unlabeled data in a systematic fashion. Unlike previous work using diffusion kernels and Gaussian random field kernels, a nonparametric kernel approach is presented that incorporates order constraints during optimization. This results in flexible kernels and avoids the need to choose among different parametric forms. Our approach relies on a quadratically constrained quadratic program (QCQP), and is computationally feasible for large datasets. We evaluate the kernels on real datasets using support vector machines, with encouraging results.
6108756
Whole-sentence exponential language models: a vehicle for linguistic-statistical integration
We introduce an exponential language model which models a whole sentence or utterance as a single unit. By avoiding the chain rule, the model treats each sentence as a “bag of features", where features are arbitrary computable properties of the sentence. The new model is computationally more efficient, and more naturally suited to modeling global sentential phenomena, than the conditional exponential (e.g. maximum entropy) models proposed to date. Using the model is straightforward. Training the model requires sampling from an exponential distribution. We describe the challenge of applying Monte Carlo Markov Chain and other sampling techniques to natural language, and discuss smoothing and step-size selection. We then present a novel procedure for feature selection, which exploits discrepancies between the existing model and the training corpus. We demonstrate our ideas by constructing and analysing competitive models in the Switchboard and Broadcast News domains, incorporating lexical and syntactic information.
7395446
Segmenting hands of arbitrary color
Hand segmentation is a prerequisite for many gesture recognition tasks. Color has been widely used for hand segmentation. However, many approaches rely on predefined skin color models. It is very difficult to predefine a color model in a mobile application where the light condition may change dramatically over time. We propose a novel statistical approach to hand segmentation based on Bayes decision theory. The proposed method requires no predefined skin color model. Instead it generates a hand color model and a background color model for a given image, and uses these models to classify each pixel in the image as either a hand pixel or a background pixel. Models are generated using a Gaussian mixture model with the restricted EM algorithm. Our method is capable of segmenting hands of arbitrary color in a complex scene. It performs well even when there is a significant overlap between hand and background colors, or when the user wears gloves. We show that the Bayes decision method is superior to a commonly used method by comparing their upper bound performance. Experimental results demonstrate the feasibility of the proposed method.
14278266
Harmonic mixtures: combining mixture models and graph-based methods for inductive and scalable semi-supervised learning
Graph-based methods for semi-supervised learning have recently been shown to be promising for combining labeled and unlabeled data in classification problems. However, inference for graph-based methods often does not scale well to very large data sets, since it requires inversion of a large matrix or solution of a large linear program. Moreover, such approaches are inherently transductive, giving predictions for only those points in the unlabeled set, and not for an arbitrary test point. In this paper a new approach is presented that preserves the strengths of graph-based semi-supervised learning while overcoming the limitations of scalability and non-inductive inference, through a combination of generative mixture models and discriminative regularization using the graph Laplacian. Experimental results show that this approach preserves the accuracy of purely graph-based transductive methods when the data has "manifold structure," and at the same time achieves inductive learning with significantly reduced computational cost.
2122679
Combining proactive and reactive predictions for data streams
Mining data streams is important in both science and commerce. Two major challenges are (1) the data may grow without limit so that it is difficult to retain a long history; and (2) the underlying concept of the data may change over time. Different from common practice that keeps recent raw data, this paper uses a measure of conceptual equivalence to organize the data history into a history of concepts. Along the journey of concept change, it identifies new concepts as well as re-appearing ones, and learns transition patterns among concepts to help prediction. Different from conventional methodology that passively waits until the concept changes, this paper incorporates proactive and reactive predictions. In a proactive mode, it anticipates what the new concept will be if a future concept change takes place, and prepares prediction strategies in advance. If the anticipation turns out to be correct, a proper prediction model can be launched instantly upon the concept change. If not, it promptly resorts to a reactive mode: adapting a prediction model to the new data. A system RePro is proposed to implement these new ideas. Experiments compare the system with representative existing prediction methods on various benchmark data sets that represent diversified scenarios of concept change. Empirical evidence demonstrates that the proposed methodology is an effective and efficient solution to prediction for data streams.
12029215
Multiuser Detection and Channel Estimation in MIMO OFDM Systems via Blind Source Separation
This paper proposes a blind multiuser detection and channel estimation method for multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems based on blind source separation (BSS). Using multiple antennas for transmission and reception the multiuser detection problem can be cast as a BSS problem of linear instantaneous mixtures. The proposed method uses BSS in each subcarrier to detect the user signals. The source order and scaling indeterminacies inherent to BSS methods are overcome by exploiting the cross correlation between the subcarriers introduced by convolutional encoding. Additionally, the method provides an estimate of the channels. It is shown that this method provides significant performance enhancement over previous work.
17903434
Circular chromatic number of Kneser graphs
This paper proves that for any positive integer n, if m is large enough, then the reduced Kneser graph KG2(m,n) has its circular chromatic number equal its chromatic number. This answers a question of Lih and Liu (J. Graph Theory 41 (2002) 62). For Kneser graphs, we prove that if m≥2n2(n - 1), then KG(m,n) has its circular chromatic number equal its chromatic number. This provides strong support for a conjecture of Johnson, Holroyd, and Stahl (J. Graph Theory 26 (1997) 137).
6927660
Scalable Representative Instance Selection and Ranking
Finding a small set of representative instances for large datasets can bring various benefits to data mining practitioners so they can (1) build a learner superior to the one constructed from the whole massive data; and (2) avoid working on the whole original dataset all the time. We propose in this paper a scalable representative instance selection and ranking (SRISTAR pronounced 3STAR) mechanism, which carries two unique features: (1) it provides a representative instance ranking list, so that users can always select instances from the top to the bottom, based on the number of examples they prefer; and (2) it investigates the behaviors of the underlying examples for instance selection, and the selection procedure tries to optimize the expected future error. Given a dataset, we first cluster instances into small data cells, each of which consists of instances with similar behaviors. Then we progressively evaluate data cells and their combinations, and order them into a list such that the learners built from the top cells are more accurate
1251013
Congestion-aware rate allocation for multipath video streaming over ad hoc wireless networks
For multipath video streaming over ad hoc wireless networks, received video quality is influenced by both the encoder performance and the delayed packet arrivals due to limited bandwidth. We propose a rate allocation scheme to optimize the expected received video quality based on simple models of encoder rate-distortion performance and network rate-congestion tradeoffs. Given the embedded bit-streams of a group of video frames and the cross traffic condition on a given set of paths, a gradient descent method is used to determine the amount of bits to be transmitted over the multiple paths for each frame. Two rate allocation strategies, layered representation and partial repetition are also investigated. Simulation results of video streaming over a 15-node wireless ad hoc network demonstrates the benefit of performing rate allocation optimization over a simple heuristic scheme. It is also shown that while layered representation is more efficient for mild delay constraints, partial repetition is more robust against packet losses.