Pengaruh Algoritma Sequential Minimal Optimization pada Support Vector Machine untuk Klasifikasi Data (Influence of Sequential Minimal Optimization Algorithm On Support Vector Machine for Data Classification)

ABSTRAKSI: Support vector machine merupakan salah satu metode supervised learning yang biasanya digunakan untuk klasifikasi data dan pada umumnya digunakan untuk menangani dataset yang memiliki dua buah kelas. Untuk memisahkan kedua kelas tersebut, digunakan sebuah bidang pemisah (hyperplane). Permasalahan muncul ketika bentuk primal dari formula pencarian hyperplane terbaik sangat sulit untuk dipecahkan, maka dari itu digunakanlah bentuk dual yang akan merubah nilai w dalam bentuk á. Permasalahan ini biasanya disebut dengan Quadratic programming. Sequential Minimal Optimization (SMO) merupakan sebuah algoritma yang dapat memecahkan quadratic programming problem (QP problem) dengan berusaha mencari nilai á dengan menggunakan analytical quadratic programming solver pada setiap langkah sehingga waktu training yang dibutuhkan lebih cepat. Dalam tugas akhir ini ditunjukkan bahwa SMO dapat melakukan waktu training yang lebih cepat dibandingkan dengan algoritma Quadratic Programming, tetapi dalam segi akurasi banyak parameter-parameter yang membuat nilai akurasi menjadi naik turun pada setiap pengujian.Kata Kunci : support vector machine, hyperplane, quadratic programming,sequential minimal optimization, bentuk primal, bentuk dualABSTRACT: Support vector machine is one method of supervised learning typically used for data classification and commonly used for handle dataset which have two classes. To separate the classes, it uses a field separator called hyperplane. Problem arise when the primal form of the formula for finding best hyperplane is very difficult to solve, hence the dual form is used to alter the value of w in the form of á. This problem is usually referred to as Quadratic programming. Sequential Minimal Optimization (SMO) is an algorithm that is used to solve quadratic programming problem (QP Problem) by trying to find the value of á with analytical quadratic programming solver in each step so the training time needed is faster. In This final project is showed that SMO can do the training time faster than Quadratic programming algorithm, but in terms of accuracy there are many parameters which makes the value of accuracy to be up and down on each test.Keyword: support vector machine, hyperplane, quadratic programming,sequential minimal optimization, primal form, dual for

Learning to distinguish hypernyms and co-hyponyms

This work is concerned with distinguishing different semantic relations which exist between distributionally similar words. We compare a novel approach based on training a linear Support Vector Machine on pairs of feature vectors with state-of-the-art methods based on distributional similarity. We show that the new supervised approach does better even when there is minimal information about the target words in the training data, giving a 15% reduction in error rate over unsupervised approaches

Simulasi dan Analisis Performansi Voice Activity Detection (VAD) Menggunakan Support Vector Machine (SVM)

ABSTRAKSI: VANET, SHWM, reactive, proactive, routing protocols, DSDV, AODV, routing overhead, normalized routing load, packet delivery ratio, packet loss ratio, convergence time, and NS-2Pada skripsi ini, metode klasifikasi yang digunakan untuk VAD adalah Support Vector Machine dengan penanganan kasus non-linier. Disini akan diadopsi Sequential Minimal Optimization (SMO) untuk menangani data training yang besar. Pengukuran performansi dilakukan dengan perhitungan persentase error yaitu dengan SDER, NDER, dan OVER.Berdasarkan hasil pengujian, Support Vector Machine menunjukkan hasil yang baik untuk menangani VAD dengan jenis derau dan SNR yang bervariasi yaitu menghasilkan persentase error untuk SDER, NDER, dan OVER kurang dari 10 % untuk SNR lebih dari 0 dB.Kata Kunci : voice activity detection, klasifikasi, support vector machine, sequential minimal optimizationABSTRACT: Voice Activity Detection (VAD) is a detector that used to clasify signal into two periode, active speech and non-active speech. VAD has been implemented in various speech communication system to reduce transmission rate in the speech transmission. There are a lot of algorithmsto implement VAD, but most of them fail to classify when the noise increased.This final project will use Support Vector Machine classifier method with non-linier problem. Sequential Minimal Optimization (SMO) is adopted to solve the large training data. Performance measurement is done by calculating the error percentage like SDER, NDER, and OVER.From the experimental result, Support Vector Machine show a good result for VAD decision in the different noise and SNR level that is less than 10 % of error percentage of SDER, NDER, and OVER in more than 0 dB SNR levels.Keyword: voice activity detection, classification, support vector machine, sequential minimal optimizatio

A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi-view Learning

This paper presents a general vector-valued reproducing kernel Hilbert spaces (RKHS) framework for the problem of learning an unknown functional dependency between a structured input space and a structured output space. Our formulation encompasses both Vector-valued Manifold Regularization and Co-regularized Multi-view Learning, providing in particular a unifying framework linking these two important learning approaches. In the case of the least square loss function, we provide a closed form solution, which is obtained by solving a system of linear equations. In the case of Support Vector Machine (SVM) classification, our formulation generalizes in particular both the binary Laplacian SVM to the multi-class, multi-view settings and the multi-class Simplex Cone SVM to the semi-supervised, multi-view settings. The solution is obtained by solving a single quadratic optimization problem, as in standard SVM, via the Sequential Minimal Optimization (SMO) approach. Empirical results obtained on the task of object recognition, using several challenging datasets, demonstrate the competitiveness of our algorithms compared with other state-of-the-art methods.Comment: 72 page