Modern object detectors usually suffer from low accuracy issues, as foregrounds always drown in tons of back-grounds and become hard examples during training. Compared with those proposal-based ones, real-time detectors are in far more serious trouble since they renounce the use of region-proposing stage which is used to filter a majority of back-grounds for achieving real-time rates. Though foregrounds as hard examples are in urgent need of being mined from tons of backgrounds, a considerable number of state-of-the-art real-time detectors, like YOLO series, have yet to profit from existing hard example mining methods, as using these methods need detectors fit series of prerequisites. In this paper, we propose a general hard example mining method named Loss Rank Mining (LRM) to fill the gap. LRM is a general method for real-time detectors, as it utilizes the final feature map which exists in all real-time detectors to mine hard examples. By using LRM, some elements representing easy examples in final feature map are filtered and detectors are forced to concentrate on hard examples during training. Extensive experiments validate the effectiveness of our method. With our method, the improvements of YOLOv2 detector on auto-driving related dataset KITTI and more general dataset PASCAL VOC are over 5 and 2 mAP, respectively. In addition, LRM is the first hard example mining strategy which could fit YOLOv2 perfectly and make it better applied in series of real scenarios where both real-time rates and accurate detection are strongly demanded.
Another hard exampling mining technique named boostrapping is used to train Support Vector Machines(SVMs) @cite_21 and hard examples are mined several times in this case. A working set containing a tiny number of samples is used in boostrapping. Samples are added to and removed from this set according to some specific rules. Processes of training model to convergence on the existing working set and utilizing the trained model to modify the working set are finished alternatively. When modifying the working set, samples in the working set classified correctly by the existing model are removed from it while samples out of the working set misclassified by the model are added to it.
@cite_21
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We show via an equivalence of mathematical programs that a support vector (SV) algorithm can be translated into an equivalent boosting-like algorithm and vice versa. We exemplify this translation procedure for a new algorithm: one-class leveraging, starting from the one-class support vector machine (1-SVM). This is a first step toward unsupervised learning in a boosting framework. Building on so-called barrier methods known from the theory of constrained optimization, it returns a function, written as a convex combination of base hypotheses, that characterizes whether a given test point is likely to have been generated from the distribution underlying the training data. Simulations on one-class classification problems demonstrate the usefulness of our approach. Support Vector Machine (SVM) is one of the most popular and effective classification algorithms and has attracted much attention in recent years. As an important large margin classifier, SVM dedicates to find the optimal separating hyperplane between two classes, thus can give outstanding generalization ability for it. In order to find the optimal hyperplane, we commonly take most of the labeled records as our training set. However, the separating hyperplane is only determined by a few crucial samples (Support Vectors, SVs), we needn't train SVM model on the whole training set. This paper presents a novel approach based on clustering algorithm, in which only a small subset was selected from the original training set to act as our final training set. Our algorithm works to select the most informative samples using K-means clustering algorithm, and the SVM classifier is built through training on those selected samples. Experiments show that our approach greatly reduces the scale of training set, thus effectively saves the training and predicting time of SVM, and at the same time guarantees the generalization performance. In this work we present a novel unsupervised framework for hard training example mining. The only input to the method is a collection of images relevant to the target application and a meaningful initial representation, provided e.g. by pre-trained CNN. Positive examples are distant points on a single manifold, while negative examples are nearby points on different manifolds. Both types of examples are revealed by disagreements between Euclidean and manifold similarities. The discovered examples can be used in training with any discriminative loss. The method is applied to unsupervised fine-tuning of pre-trained networks for fine-grained classification and particular object retrieval. Our models are on par or are outperforming prior models that are fully or partially supervised. One of the surprising recurring phenomena observed in experiments with boosting is that the test error of the generated classifier usually does not increase as its size becomes very large, and often is observed to decrease even after the training error reaches zero. In this paper, we show that this phenomenon is related to the distribution of margins of the training examples with respect to the generated voting classification rule, where the margin of an example is simply the difference between the number of correct votes and the maximum number of votes received by any incorrect label. We show that techniques used in the analysis of Vapnik's support vector classifiers and of neural networks with small weights can be applied to voting methods to relate the margin distribution to the test error. We also show theoretically and experimentally that boosting is especially effective at increasing the margins of the training examples. Finally, we compare our explanation to those based on the bias-variance decomposition. We study the use of support vector machines (SVM) in classifying e-mail as spam or nonspam by comparing it to three other classification algorithms: Ripper, Rocchio, and boosting decision trees. These four algorithms were tested on two different data sets: one data set where the number of features were constrained to the 1000 best features and another data set where the dimensionality was over 7000. SVM performed best when using binary features. For both data sets, boosting trees and SVM had acceptable test performance in terms of accuracy and speed. However, SVM had significantly less training time. Support vector machines (SVM) have been highly successful in many machine learning problems. Recently, it is also used for multi-instance (MI) learning by employing a kernel that is defined directly on the bags. As only the bags (but not the instances) have known labels, this MI kernel implicitly assumes all instances in the bag to be equally important. However, a fundamental property of MI learning is that not all instances in a positive bag necessarily belong to the positive class, and thus different instances in the same bag should have different contributions to the kernel. In this paper, we address this instance label ambiguity by using the method of marginalized kernels. It first assumes that all the instance labels are available and defines a label-dependent kernel on the instances. By integrating out the unknown instance labels, a marginalized kernel defined on the bags can then be obtained. A desirable property is that this kernel weights the instance pairs by the consistencies of their probabilistic instance labels. Experiments on both classification and regression data sets show that this marginalized MI kernel, when used in a standard SVM, performs consistently better than the original MI kernel. It also outperforms a number of traditional MI learning methods. We propose a pre-selection method for training support vector machines (SVM) with a large-scale dataset. Specifically, the proposed method selects patterns around the class boundary and the selected data is fed to train an SVM. For the selection, that is, searching for boundary patterns, we utilize a relative neighborhood graph (RNG). An RNG has an edge for each pair of neighboring patterns and thus, we can find boundary patterns by looking for edges connecting patterns from different classes. Through large-scale handwritten digit pattern recognition experiments, we show that the proposed pre-selection method accelerates SVM training process 5–15 times faster without degrading recognition accuracy. We introduce a semi-supervised support vector machine (S3VM) method. Given a training set of labeled data and a working set of unlabeled data, S3VM constructs a support vector machine using both the training and working sets. We use S3VM to solve the transduction problem using overall risk minimization (ORM) posed by Vapnik. The transduction problem is to estimate the value of a classification function at the given points in the working set. This contrasts with the standard inductive learning problem of estimating the classification function at all possible values and then using the fixed function to deduce the classes of the working set data. We propose a general S3VM model that minimizes both the misclassification error and the function capacity based on all the available data. We show how the S3VM model for 1-norm linear support vector machines can be converted to a mixed-integer program and then solved exactly using integer programming. Results of S3VM and the standard 1-norm support vector machine approach are compared on ten data sets. Our computational results support the statistical learning theory results showing that incorporating working data improves generalization when insufficient training information is available. In every case, S3VM either improved or showed no significant difference in generalization compared to the traditional approach. Support Vector Machine (SVM) is an efficient data mining approach for data classification. However, SVM algorithm requires very large memory requirement and computational time to deal with very large dataset. To reduce the computational time during the process of training the SVM, a combination of distributed and parallel computing method, CoDLib have been proposed. Instead of using a single machine for parallel computing, multiple machines in a cluster are used. Message Passing Interface (MPI) is used in the communication between machines in the cluster. The original dataset is split and distributed to the respective machines. Experiments results shows a great speed up on the training of the MNIST dataset where training time has been significantly reduced compared with standard LIBSVM without affecting the quality of the SVM. Using discriminative classifiers, such as Support Vector Machines (SVMs) in combination with, or as an alternative to, Hidden Markov Models (HMMs) has a number of advantages for difficult speech recognition tasks. For example, the models can make use of additional dependencies in the observation sequences than HMMs provided the appropriate form of kernel is used. However standard SVMs are binary classifiers, and speech is a multi-class problem. Furthermore, to train SVMs to distinguish word pairs requires that each word appears in the training data. This paper examines both of these limitations. Tree-based reduction approaches for multiclass classification are described, as well as some of the issues in applying them to dynamic data, such as speech. To address the training data issues, a simplified version of HMM-based synthesis can be used, which allows data for any word-pair to be generated. These approaches are evaluated on two noise corrupted digit sequence tasks: AURORA 2.0; and actual in-car collected data. The Support Vector Machine (SVM) is a new and very promising classification technique developed by Vapnik and his group at AT &T Bell Labs. This new learning algorithm can be seen as an alternative training technique for Polynomial, Radial Basis Function and Multi-Layer Perceptron classifiers. An interesting property of this approach is that it is an approximate implementation of the Structural Risk Minimization (SRM) induction principle. The derivation of Support Vector Machines, its relationship with SRM, and its geometrical insight, are discussed in this paper. Training a SVM is equivalent to solve a quadratic programming problem with linear and box constraints in a number of variables equal to the number of data points. When the number of data points exceeds few thousands the problem is very challenging, because the quadratic form is completely dense, so the memory needed to store the problem grows with the square of the number of data points. Therefore, training problems arising in some real applications with large data sets are impossible to load into memory, and cannot be solved using standard non-linear constrained optimization algorithms. We present a decomposition algorithm that can be used to train SVM''s over large data sets. The main idea behind the decomposition is the iterative solution of sub-problems and the evaluation of, and also establish the stopping criteria for the algorithm. We present previous approaches, as well as results and important details of our implementation of the algorithm using a second-order variant of the Reduced Gradient Method as the solver of the sub-problems. As an application of SVM''s, we present preliminary results we obtained applying SVM to the problem of detecting frontal human faces in real images. Support vector machines (SVMs) have been promising methods for classification and regression analysis because of their solid mathematical foundations which convery several salient properties that other methods hardly provide. However, despite the prominent properties of SVMs, they are not as favored for large-scale data mining as for pattern recognition or machine learning because the training complexity of SVMs is highly dependent on the size of a data set. Many real-world data mining applications involve millions or billions of data records where even multiple scans of the entire data are too expensive to perform. This paper presents a new method, Clustering-Based SVM (CB-SVM), which is specifically designed for handling very large data sets. CB-SVM applies a hierarchical micro-clustering algorithm that scans the entire data set only once to provide an SVM with high quality samples that carry the statistical summaries of the data such that the summaries maximize the benefit of learning the SVM. CB-SVM tries to generate the best SVM boundary for very large data sets given limited amount of resources. Our experiments on synthetic and real data sets show that CB-SVM is highly scalable for very large data sets while also generating high classification accuracy. Decomposition techniques are used to speed up training support vector machines but for linear programming support vector machines (LP-SVMs) direct implementation of decomposition techniques leads to infinite loops. To solve this problem and to further speed up training, in this paper, we propose an improved decomposition techniques for training LP-SVMs. If an infinite loop is detected, we include in the next working set all the data in the working sets that form the infinite loop. To further accelerate training, we improve a working set selection strategy: at each iteration step, we check the number of violations of complementarity conditions and constraints. If the number of violations increases, we conclude that the important data are removed from the working set and restore the data into the working set. The computer experiments demonstrate that training by the proposed decomposition technique with improved working set selection is drastically faster than that without using the decomposition technique. Furthermore, it is always faster than that without improving the working set selection for all the cases tested. We describe an algorithm for support vector machines (SVM) that can be parallelized efficiently and scales to very large problems with hundreds of thousands of training vectors. Instead of analyzing the whole training set in one optimization step, the data are split into subsets and optimized separately with multiple SVMs. The partial results are combined and filtered again in a 'Cascade' of SVMs, until the global optimum is reached. The Cascade SVM can be spread over multiple processors with minimal communication overhead and requires far less memory, since the kernel matrices are much smaller than for a regular SVM. Convergence to the global optimum is guaranteed with multiple passes through the Cascade, but already a single pass provides good generalization. A single pass is 5x - 10x faster than a regular SVM for problems of 100,000 vectors when implemented on a single processor. Parallel implementations on a cluster of 16 processors were tested with over 1 million vectors (2-class problems), converging in a day or two, while a regular SVM never converged in over a week. Linear support vector machines (SVM) are useful for classifying large-scale sparse data. Problems with sparse features are common in applications such as document classification and natural language processing. In this paper, we propose a novel coordinate descent algorithm for training linear SVM with the L2-loss function. At each step, the proposed method minimizes a one-variable sub-problem while fixing other variables. The sub-problem is solved by Newton steps with the line search technique. The procedure globally converges at the linear rate. As each sub-problem involves only values of a corresponding feature, the proposed approach is suitable when accessing a feature is more convenient than accessing an instance. Experiments show that our method is more efficient and stable than state of the art methods such as Pegasos and TRON. Support vector machines (SVMs) are learning systems based on the statistical learning theory, which are exhibiting good generalization ability on real data sets. Nevertheless, a possible limitation of SVM is that they generate black box models. In this work, a procedure for rule extraction from support vector machines is proposed: the SVM+Prototypes method. This method allows to give explanation ability to SVM. Once determined the decision function by means of a SVM, a clustering algorithm is used to determine prototype vectors for each class. These points are combined with the support vectors using geometric methods to define ellipsoids in the input space, which are later transfers to if-then rules. By using the support vectors we can establish the limits of these regions. We investigate the relation of two fundamental tools in machine learning and signal processing, that is the support vector machine (SVM) for classification, and the Lasso technique used in regression. We show that the resulting optimization problems are equivalent, in the following sense. Given any instance of an L2-loss soft-margin (or hard-margin) SVM, we construct a Lasso instance having the same optimal solutions, and vice versa. As a consequence, many existing optimization algorithms for both SVMs and Lasso can also be applied to the respective other problem instances. Also, the equivalence allows for many known theoretical insights for SVM and Lasso to be translated between the two settings. One such implication gives a simple kernelized version of the Lasso, analogous to the kernels used in the SVM setting. Another consequence is that the sparsity of a Lasso solution is equal to the number of support vectors for the corresponding SVM instance, and that one can use screening rules to prune the set of support vectors. Furthermore, we can relate sublinear time algorithms for the two problems, and give a new such algorithm variant for the Lasso. We also study the regularization paths for both methods. We investigate a family of poisoning attacks against Support Vector Machines (SVM). Such attacks inject specially crafted training data that increases the SVM's test error. Central to the motivation for these attacks is the fact that most learning algorithms assume that their training data comes from a natural or well-behaved distribution. However, this assumption does not generally hold in security-sensitive settings. As we demonstrate, an intelligent adversary can, to some extent, predict the change of the SVM's decision function due to malicious input and use this ability to construct malicious data. The proposed attack uses a gradient ascent strategy in which the gradient is computed based on properties of the SVM's optimal solution. This method can be kernelized and enables the attack to be constructed in the input space even for non-linear kernels. We experimentally demonstrate that our gradient ascent procedure reliably identifies good local maxima of the non-convex validation error surface, which significantly increases the classifier's test error. A computationally efficient approach to local learning with kernel methods is presented. The Fast Local Kernel Support Vector Machine (FaLK-SVM) trains a set of local SVMs on redundant neighbourhoods in the training set and an appropriate model for each query point is selected at testing time according to a proximity strategy. Supported by a recent result by Zakai and Ritov (2009) relating consistency and localizability, our approach achieves high classification accuracies by dividing the separation function in local optimisation problems that can be handled very efficiently from the computational viewpoint. The introduction of a fast local model selection further speeds-up the learning process. Learning and complexity bounds are derived for FaLK-SVM, and the empirical evaluation of the approach (with data sets up to 3 million points) showed that it is much faster and more accurate and scalable than state-of-the-art accurate and approximated SVM solvers at least for non high-dimensional data sets. More generally, we show that locality can be an important factor to sensibly speed-up learning approaches and kernel methods, differently from other recent techniques that tend to dismiss local information in order to improve scalability. A challenge for statistical learning is to deal with large data sets, e.g. in data mining. The training time of ordinary Support Vector Machines is at least quadratic, which raises a serious research challenge if we want to deal with data sets of millions of examples. We propose a "hard parallelizable mixture" methodology which yields significantly reduced training time through modularization and parallelization: the training data is iteratively partitioned by a "gater" model in such a way that it becomes easy to learn an "expert" model separately in each region of the partition. A probabilistic extension and the use of a set of generative models allows representing the gater so that all pieces of the model are locally trained. For SVMs, time complexity appears empirically to local growth linearly with the number of examples, while generalization performance can be enhanced. For the probabilistic version of the algorithm, the iterative algorithm probably goes down in a cost function that is an upper bound on the negative log-likelihood.