SUPPORT VECTOR MACHINE BINARY CLASSIFICATION MATLAB CODE

Tìm thấy 10,000 tài liệu liên quan tới từ khóa "SUPPORT VECTOR MACHINE BINARY CLASSIFICATION MATLAB CODE":

Model-Based Design for Embedded Systems- P7 pps

MODEL-BASED DESIGN FOR EMBEDDED SYSTEMS- P7 PPS

additional cycles can be needed if a correctly predicted branch is taken, asthe branch target has to be calculated and loaded in the program counter.This problem is solved by implementing a model of the branch predictionand by a comparison of the predicted branch behavior with the executedbranch behavior. If dynamic branch prediction is used, a model of the under-lying state machine is implemented and its results are compared with theexecuted branch behavior. The cycle count of each possible case is calcu-lated and added to the cumulative cycle count before the next basic block isentered.2.4.5.2 Instruction CacheFigure 2.3 shows that for the simulation of the instruction cache, every basicblock of the translated program has to be divided into several cache analysisblocks. This has to be done until the tag changes or the basic block ends. Afterthat, a function call to the cache handling model is added. This code uses acache model to find out possible cache hits or misses.The cache simulation will be explained in more detail in the next fewparagraphs. This explanation will start with a description of the cachemodel.Nicolescu/Model-Based Design for Embedded Systems 67842_C002 Finals Page 44 2009-10-1344 Model-Based Design for Embedded SystemscycleCalcICacheC program Binary code Cache modeldatalrutagvasm_inst1asm_instl+1asm_instn
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Báo cáo khoa học: "A Novel Discourse Parser Based on Support Vector Machine Classification" docx

BÁO CÁO KHOA HỌC A NOVEL DISCOURSE PARSER BASED ON SUPPORT VECTOR MACHINE CLASSIFICATION DOCX

sub-trees are merged into a new sub-tree, onlyconnections with adjacent spans on each side areaffected, and therefore, only two new scores needto be computed. Since our SVM classifiers workin linear time, the overall time-complexity of ouralgorithm is O(n).3 FeaturesInstrumental to our system’s performance isthe choice of a set of salient characteristics(“features”) to be used as input to the SVMalgorithm for training and classification. Once thefeatures are determined, classification instancescan be formally represented as a vector of valuesin R.We use n-fold validation on S and L classifiersto assess the impact of some sets of featureson general performance and eliminate redundantfeatures. However, we worked under the (verified)assumption that SVMs’ capacity to handle high-dimensional data and resilience to input noise limitthe negative impact of non-useful features.In the following list of features, obtainedempirically by trial-and-error, features suffixed by‘S[pan]’ are sub-tree-specific features, symmetri-cally extracted from both left and right candidatespans. Features suffixed by ‘F[ull]’ are a functionof the two sub-trees considered as a pair. Multi-label features are turned into sets of binary valuesand trees use a trivial fixed-length binary encodingthat assumes fixed depth.
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Tài liệu Báo cáo khoa học: "N Semantic Classes are Harder than Two" doc

TÀI LIỆU BÁO CÁO KHOA HỌC N SEMANTIC CLASSES ARE HARDER THAN TWO DOC

ReferencesPeter G. Anick. 2003. Using terminological feedback forweb search refinement: a log-based study. In SIGIR 2003,pages 88–95.Ido Dagan, Oren Glickman, and Bernardo Magnini. 2005.The pascal recognising textual entailment challenge. InPASCAL Challenges Workshop on Recognising TextualEntailment.Ted E. Dunning. 1993. Accurate methods for the statisticsof surprise and coincidence. Computational Linguistics,19(1):61–74.Marti A. Hearst. 1992. Automatic acquisition of hyponymsfrom large text corpora. In Proceedings of Coling 1992,pages 539–545.Rosie Jones, Benjamin Rey, Omid Madani, and WileyGreiner. 2006. Generating query substitutions. In 15thInternational World Wide Web Conference (WWW-2006),Edinburgh.Sathiya Keerthi and Dennis DeCoste. 2005. A modified fi-nite newton method for fast solution of large scale linearsvms. Journal of Machine Learning Research, 6:341–361,March.Lillian Lee. 1999. Measures of distributional similarity. In37th Annual Meeting of the Association for ComputationalLinguistics, pages 25–32.V. I. Levenshtein. 1966. Binary codes capable of cor-recting deletions, insertions, and reversals. Cyberneticsand Control Theory, 10(8):707–710. Original in DokladyAkademii Nauk SSSR 163(4): 845–848 (1965).Dekang Lin. 1998. Dependency-based evaluation of mini-
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Aerospace Technologies Advancements Fig Part 1 ppt

AEROSPACE TECHNOLOGIES ADVANCEMENTS FIG PART 1 PPT

words they are non-parametric, non-linear and multivariate learning algorithms. The uses of machine learning to date have fallen into three basic categories which are widely applicable across all of the Geosciences and remote sensing, the first two categories use machine learning for its regression capabilities, the third category uses machine learning for its classification capabilities. We can characterize the three application themes are as follows: First, where we have a theoretical description of the system in the form of a deterministic Aerospace Technologies Advancements 2 model, but the model is computationally expensive. In this situation, a machine-learning “wrapper” can be applied to the deterministic model providing us with a “code accelerator”. A good example of this is in the case of atmospheric photochemistry where we need to solve a large coupled system of ordinary differential equations (ODEs) at a large grid of locations. It was found that applying a neural network wrapper to the system was able to provide a speed up of between a factor of 2 and 200 depending on the conditions. Second, when we do not have a deterministic model but we have data available enabling us to empirically learn the behaviour of the system. Examples of this would include: Learning inter-instrument bias between sensors with a temporal overlap, and inferring physical parameters from remotely sensed proxies. Third, machine learning can be used for classification, for example, in providing land surface type classifications. Support Vector Machines perform particularly well for classification problems. Now that we have an overview of the typical applications, the sections that follow will introduce two of the most powerful machine learning approaches, neural networks and support vector machines and then present a variety of examples. 3. Machine learning 3.1 Neural networks Neural networks are multivariate, non-parametric, ‘learning’ algorithms (Haykin, 1994, Bishop, 1995, 1998, Haykin, 2001a, Haykin, 2001b, 2007) inspired by biological neural networks. Computational neural networks (NN) consist of an interconnected group of artificial neurons that processes information in parallel using a connectionist approach to
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Báo cáo Bài tập lớn Matlab Vật lý 1

BÁO CÁO BÀI TẬP LỚN MATLAB VẬT LÝ 1

CODE MATLAB, bài tập lớn vật lý, matlab vật lý đề tài 9, thế năng, năng lượng, động năng, matlab, bài tập lớn, lực thế, đề tài 9, vật lý, đại học, đại học bách khoa, báo cáo đầy đủ matlab vật lý đại cương A1 đề tài số 9 lực và thế năng đầy đủ full Đây là bản báo cáo đầy đủ với 2 code vật lý của đề tài.

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ElectrCircuitAnalysisUsingMATLAB Phần 8

ELECTRCIRCUITANALYSISUSINGMATLAB PHẦN 8

(8.20) the response at the output of the system is y t H jnw c jnw tnnon o( ) ( ) exp( )==−∞∞∑ (8.21) The following two examples show how to use MATLAB to obtain the coeffi-cients of Fourier series expansion. Example 8.2 For the full-wave rectifier waveform shown in Figure 8.3, the period is 0.0333s and the amplitude is 169.71 Volts. (a) Write a MATLAB program to obtain the exponential Fourier series coefficients cn for n = 0,1, 2, .. , 19

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image classification using support vector

IMAGE CLASSIFICATION USING SUPPORT VECTOR

decision about the land cover class and require a training sample. On the contrary, clustering based algorithm, e.g. K-mean, K-NN or ISODATA, are unsupervised classifier, and fuzzy-set classifier are soft classification providing more information and potentially a more accurate result. Besides, the knowledge based classification, using knowledge and rules from expert, or generating rules from observed data, is becoming attractive. We refer to D. Lu and Q. Weng [1] for complete treatment of image classification approaches. In recent years, combine of multiple classifiers have received much attention. Some researchers combine NN classifier [9], SVM classifier [10] or AdaBoost classifier for image classification. The aim of this paper is bring together two areas in which are Artificial Neural Network (ANN) and Support Vector Machine (SVM) applying for image classification. 3. A novel combination model (ANN_SVM) apply for image classification After the images were preprocessed and extracted features, they would present in the large representation space. Thus, they would be projected into the Sub-space in order to analysis easily and reduce dimensions of image’s feature.
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Báo cáo khoa học: "Beam-Width Prediction for Efficient Context-Free Parsing" pot

BÁO CÁO KHOA HỌC BEAM WIDTH PREDICTION FOR EFFICIENT CONTEXT FREE PARSING POT

Closure for the same sentence; the number of com-pletely open cells increases somewhat, but the totalnumber of open cells (including those open to fac-tored categories) is greatly reduced.We compare the effectiveness of Constituent Clo-sure, Complete Closure, and Chart Constraints, bydecreasing the percentage of chart cells closed un-til accuracy over all sentences in our developmentset start to decline. For Constituent and CompleteClosure, we also vary the loss function, adjustingthe relative penalty between a false-negative (clos-ing off a chart cell that contains a maximum like-lihood edge) and a false-positive. Results show thatusing Chart Constrains as a baseline, we prune (skip)33% of the total chart cells. Constituent Closure im-proves on this baseline only slightly (36%), but wesee our biggest gains with Complete Closure, whichprunes 56% of all chart cells in the development set.All of these open/closed cell classification meth-ods can improve the efficiency of the exhaustiveCYK algorithm, or any of the approximate infer-ence methods mentioned in Section 2. We empir-ically evaluate them when applied to CYK parsingand beam-search parsing in Section 6.4 Beam-Width PredictionThe cell-closing approaches discussed in Section 3make binary decisions to either allow or completelyblock all edges in each cell. This all-on/all-off tacticignores the characteristics of the local cell popula-tion, which, given a large statistical grammar, may
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Tài liệu Electronics and Circuit Analysis Using MATLAB P8 ppt

TÀI LIỆU ELECTRONICS AND CIRCUIT ANALYSIS USING MATLAB P8 PPT

(8.20) the response at the output of the system is y t H jnw c jnw tnnon o( ) ( ) exp( )==−∞∞∑ (8.21) The following two examples show how to use MATLAB to obtain the coeffi-cients of Fourier series expansion. Example 8.2 For the full-wave rectifier waveform shown in Figure 8.3, the period is 0.0333s and the amplitude is 169.71 Volts. (a) Write a MATLAB program to obtain the exponential Fourier series coefficients cn for n = 0,1, 2, .. , 19

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Báo cáo hóa học: " Research Article Incremental Support Vector Machine Framework for Visual Sensor Networks" pptx

BÁO CÁO HÓA HỌC: " RESEARCH ARTICLE INCREMENTAL SUPPORT VECTOR MACHINE FRAMEWORK FOR VISUAL SENSOR NETWORKS" PPTX

Several studies related to human motion classificationand visual sensor networks have been published. The studyof novel extraction methods and motion tracking is poten-tially a standalone topic [22–27]. Different sensor netw o rkarchitectures were proposed to enable dynamic system archi-tecture (Matsuyama et al. [25]), real time v isual surveillancesystem (Haritaoglu et al. [26]), wide human tracking area(Nakazawa et al. [27]), and integrated system of active cam-era network for human tracking and face recognition (Sogoet al. [28]).The scope of this paper is not to propose novel feature ex-traction techniques and motion detection. Our main objec-tive is to demonstrate machine learning in visual sensor net-works using our incremental SVM methodology. During theincremental learning phase, sensor nodes need to performlocal model verification. For instance, if xN+1is the recentlyacquired frame sequence that needs to be classified, our pro-posed strategy would entail the following steps highlightedin Algorithm 1.5.2. Cluster head no de operationsThe cluster head is expected to t rigger the model updatesbased on an efficient meta-analysis and aggregate protocol.A properly selected aggregation procedure can be superior toa single classifier whose output is based on a decision fusionof all the different classification results of the network sensornodes [29].The generic cluster head architecture is outlined in Figure6.
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Tài liệu Electronics Circuit Analysys Using Matlab P8 pdf

TÀI LIỆU ELECTRONICS CIRCUIT ANALYSYS USING MATLAB P8 PDF

n obtained from part (b). 8.3 For the half-wave rectifier waveform, shown in Figure P8.3, with a period of 0.01 s and a peak voltage of 17 volts. (a) Write a MATLAB program to obtain the exponential Fourier series coefficients cn for n = 0, 1, , 20. (b) Plot the amplitude spectrum. (c) Using the values obtained in (a), use MATLAB to regenerate the approximation togt() when 20 terms of the exponential Fourier series are used. © 1999 CRC Press LLC © 1999 CRC Press LLC

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Managing and Mining Graph Data part 37 pps

MANAGING AND MINING GRAPH DATA PART 37 PPS

3.1 Formulation of Graph BoostingThe name ‘boosting’ comes from the fact that linear program boosting (LP-Boost) is used as a fundamental computational framework. In chemical infor-matics experiments [40], it was shown that the accuracy of graph boosting isbetter than graph kernels. At the same time, key substructures are explicitlydiscovered.Our prediction rule is a convex combination of binary indicators 𝑥𝑖,𝑗, andhas the form𝑓(𝒙𝑖) =𝑝∈𝒫𝛽𝑝𝒙𝑖,𝑝, (3.1)where 𝜷 is a ∣𝒫∣-dimensional column vector such that𝑝∈𝒫𝛽𝑝= 1 and𝛽𝑝≥ 0.This is a linear discriminant function in an intractably large dimensional
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Visual Terrain Classification For Legged Robots

VISUAL TERRAIN CLASSIFICATION FOR LEGGED ROBOTS

Recent work in terrain classification has relied largely on 3D sensing methods and color based classification. We present an approach that works with a single, compact camera and maintains high classification rates that are robust to changes in illumination. Terrain is classified using a bag of visual words (BOVW) created from speeded up robust features (SURF) with a support vector machine (SVM) classifier. We present several novel techniques to augment this approach. A gradient descent inspired algorithm is used to adjust the SURF Hessian threshold to reach a nominal feature density. A sliding window technique is also used to classify mixed terrain images with high resolution. We demonstrate that our approach is suitable for small legged robots by performing realtime terrain classification on LittleDog. The classifier is used to select between predetermined gaits for traversing terrain of varying difficulty.
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Phương pháp support vector machines lý thuyết và ứng dụng

PHƯƠNG PHÁP SUPPORT VECTOR MACHINES LÝ THUYẾT VÀ ỨNG DỤNG

Phương pháp support vector machines lý thuyết và ứng dụng

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Báo cáo hóa học: " Performance Measure as Feedback Variable in Image Processing" potx

BÁO CÁO HÓA HỌC: " PERFORMANCE MEASURE AS FEEDBACK VARIABLE IN IMAGE PROCESSING" POTX

processing levels. The measures of image quality, represent-(a)(b)(c)Figure 15: Character recognition result achieved with the OCR sys-tem with thresholding based on 1D entropy (a), 2D entropy (b), andclosed-loop (c).ing the performance measure of corresponding closed-loopimage processing, have been determined w ith respect to theoverall system performance. It has been shown that the per-formance measure should be appropriate f rom both the im-age processing and control point of view.The choice of controlled and actuator variables while in-cluding the feedback control in image processing strongly de-pends on the image processing application and is not alwaysstraightforward due to the availability of a large number ofvariables that can be treated as measures of the quality of im-ages but which are not all appropriate as feedback variablesin closed-loop control. This fact differentiates the most con-trol in image processing from the classical industrial control.However, once a pair of controlled and actuator variables isfound for the specific application, the framework for the in-clusion of proven error-based control methods is provided.Experimental results on comparison of performance ofthe proposed thresholding method, representing the seg-mentation step, to the performances of traditional adaptivethresholding methods are presented. The results confirmedbenefit of the using of feedback information on the qualityof binary image to adjust the thresholding parameter.12 EURASIP Journal on Applied Signal Processing
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 TÌM HIỂU HDL CODER TRONG MATLAB VÀTHỰC THI BẰNG CHƯƠNG TRÌNH TÍNH LOGARITHM 2 LOG2

TÌM HIỂU HDL CODER TRONG MATLAB VÀTHỰC THI BẰNG CHƯƠNG TRÌNH TÍNH LOGARITHM 2 LOG2

“tổng hợp được”9CHUYỂN ĐỔI TỪ FLOATING-POINT SANGFIXED-POINTĐộ chính xác và tốc độ thực hiện phụ thuộc vào chiều dàiwordTự động đề xuất chiều dài word hoặc thông qua phân tích tĩnhTự động sử dụng bit tạmKiểm chứng fixed-point được phát sinh ra so với floating-point10PHÁT SINH HDL CODEPhát sinh từ fixed-point MATLAB codeHỗ trợ Verilog và VHDLPhát sinh các báo cáoTùy chọn tối ưu cho mục đích diện tích và tốc độ11KIỂM CHỨNG HDL CODEPhát sinh HDL testbench từ MATLAB testbenchTương tác với HDL Verifier nhằm đồng mô phỏng
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Báo cáo môn máy học support vectors machine

BÁO CÁO MÔN MÁY HỌC SUPPORT VECTORS MACHINE

Báo cáo môn máy học support vectors machine

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