Unlabeled pattern management through Semi-Supervised classification techniques

l'obbiettivo di questo progetto consiste nell'analizzare le performance di alcuni algoritmi di semi-supervised learning proposti negli ultimi anni. In particolare si è usato un algoritmo di feature selection basato su Self-training per determinare l'insieme ottimo di features per ogni dataset. Poi sono stati applicati alcuni algoritmi di semi-supervised learning per classificare i dati. Questi algoritmi sono stati testati usando rispettivamente come classificatore di base SVM e SMC

Master of Science

thesisPresently, speech recognition is gaining worldwide popularity in applications like Google Voice, speech-to-text reporter (speech-to-text transcription, video captioning, real-time transcriptions), hands-free computing, and video games. Research has been done for several years and many speech recognizers have been built. However, most of the speech recognizers fail to recognize the speech accurately. Consider the well-known application of Google Voice, which aids in users search of the web using voice. Though Google Voice does a good job in transcribing the spoken words, it does not accurately recognize the words spoken with different accents. With the fact that several accents are evolving around the world, it is essential to train the speech recognizer to recognize accented speech. Accent classification is defined as the problem of classifying the accents in a given language. This thesis explores various methods to identify the accents. We introduce a new concept of clustering windows of a speech signal and learn a distance metric using specific distance measure over phonetic strings to classify the accents. A language structure is incorporated to learn this distance metric. We also show how kernel approximation algorithms help in learning a distance metric

A detection-based pattern recognition framework and its applications

The objective of this dissertation is to present a detection-based pattern recognition framework and demonstrate its applications in automatic speech recognition and broadcast news video story segmentation. Inspired by the studies of modern cognitive psychology and real-world pattern recognition systems, a detection-based pattern recognition framework is proposed to provide an alternative solution for some complicated pattern recognition problems. The primitive features are first detected and the task-specific knowledge hierarchy is constructed level by level; then a variety of heterogeneous information sources are combined together and the high-level context is incorporated as additional information at certain stages. A detection-based framework is a â divide-and-conquerâ design paradigm for pattern recognition problems, which will decompose a conceptually difficult problem into many elementary sub-problems that can be handled directly and reliably. Some information fusion strategies will be employed to integrate the evidence from a lower level to form the evidence at a higher level. Such a fusion procedure continues until reaching the top level. Generally, a detection-based framework has many advantages: (1) more flexibility in both detector design and fusion strategies, as these two parts can be optimized separately; (2) parallel and distributed computational components in primitive feature detection. In such a component-based framework, any primitive component can be replaced by a new one while other components remain unchanged; (3) incremental information integration; (4) high level context information as additional information sources, which can be combined with bottom-up processing at any stage. This dissertation presents the basic principles, criteria, and techniques for detector design and hypothesis verification based on the statistical detection and decision theory. In addition, evidence fusion strategies were investigated in this dissertation. Several novel detection algorithms and evidence fusion methods were proposed and their effectiveness was justified in automatic speech recognition and broadcast news video segmentation system. We believe such a detection-based framework can be employed in more applications in the future.Ph.D.Committee Chair: Lee, Chin-Hui; Committee Member: Clements, Mark; Committee Member: Ghovanloo, Maysam; Committee Member: Romberg, Justin; Committee Member: Yuan, Min

The Impact of Emotion Focused Features on SVM and MLR Models for Depression Detection

Major depressive disorder (MDD) is a common mental health diagnosis with estimates upwards of 25% of the United States population remain undiagnosed. Psychomotor symptoms of MDD impacts speed of control of the vocal tract, glottal source features and the rhythm of speech. Speech enables people to perceive the emotion of the speaker and MDD decreases the mood magnitudes expressed by an individual. This study asks the questions: “if high level features deigned to combine acoustic features related to emotion detection are added to glottal source features and mean response time in support vector machines and multivariate logistic regression models, would that improve the recall of the MDD class?” To answer this question, a literature review goes through common features in MDD detection, especially features related to emotion recognition. Using feature transformation, emotion recognition composite features are produced and added to glottal source features for model evaluation

커널 서포트와 평형점을 활용한 차분 프라이버시 다중 클래스 분류 기법

학위논문(석사) -- 서울대학교대학원 : 공과대학 산업공학과, 2022.2. 이재욱.In this paper, we propose a multi-class classification method using kernel supports and a dynamic system under differential privacy. We find support vector machine (SVM) algorithms have a fundamental weaknesses of implementing differential privacy because the decision function depends on some subset of the training data called the support vectors. Therefore, we develop a method using interior points called equilibrium points (EPs) without relying on the decision boundary. To construct EPs, we utilize a dynamic system with a new differentially private support vector data description (SVDD) by perturbing the sphere center in the kernel space. Empirical results show that the proposed method achieves better performance even on small-sized datasets where differential privacy performs poorly.본 논문에서는 커널 서포트와 평형점을 활용한 차분 프라이버시 다중 클래스 분류 기법을 제시한다. 서포트 벡터 분류 기법은 데이터 분석과 머신 러닝에 활용성이 높아 사용자의 데이터를 보호하며 학습하는 것이 필수적이다. 그 중 가장 대중적인 서포트 벡터 머신(SVM)은 서포트 벡터라고 불리는 일부 데이터에만 분류에 의존하기 때문에 프라이버시 차분 기법을 활용하기 어렵다. 데이터 하나가 변경되었을 때 결과의 변화가 적어야 하는 차분 프라이버시 상황에서 서포트 벡터 하나가 없어진다면 분류기의 결정 경계는 그 변화에 매우 취약하다는 문제가 있다. 이 문제를 해결하기 위해 본 연구에서는 평형점이라고 불리는 군집 내부에 존재하는 점을 활용하는 차분 프라이버시 다중 클래스 분류 기법을 제시한다. 이를 위해, 먼저 커널 공간에서 구의 중심에 섭동을 더해 차분 프라이버시를 만족하는 서포트 벡터 데이터 디스크립션(SVDD)을 구하고 이를 레벨집합으로 활용해 동역학계로 극소점들을 구한다. 평형점을 활용하거나 고차원 데이터의 경우 초입방체를 만들어, 학습한 모델을 추론에 활용할 수 있는 (1) 서포트 함수를 공개 하는 방법과 (2) 평형점을 공개하는 방법을 제시한다. 8개의 다양한 데이터 집합의 실험적인 결과는 제시한 방법론이 노이즈에 강건한 내부의 점을 활용해 기존의 차분 프라이버시 서포트 벡터 머신보다 성능을 높이고, 차분 프라이버시가 적용되기 어려운 작은 데이터셋에도 활용될 수 있다는 기술임을 보여준다.Chapter 1 Introduction 1 1.1 Problem Description: Data Privacy 1 1.2 The Privacy of Support Vector Methods 2 1.3 Research Motivation and Contribution 4 1.4 Organization of the Thesis 5 Chapter 2 Literature Review 6 2.1 Differentially private Empirical risk minimization 6 2.2 Differentially private Support vector machine 7 Chapter 3 Preliminaries 9 3.1 Differential privacy 9 Chapter 4 Differential private support vector data description 12 4.1 Support vector data description 12 4.2 Differentially private support vector data description 13 Chapter 5 Differentially private multi-class classification utilizing SVDD 19 5.1 Phase I. Constructing a private support level function 20 5.2 Phase II: Differentially private clustering on the data space via a dynamical system 21 5.3 Phase III: Classifying the decomposed regions under differential privacy 22 Chapter 6 Inference scenarios and releasing the differentially private model 25 6.1 Publishing support function 26 6.2 Releasing equilibrium points 26 6.3 Comparison to previous methods 27 Chapter 7 Experiments 28 7.1 Models and Scenario setting 28 7.2 Datasets 29 7.3 Experimental settings 29 7.4 Empirical results on various datasets under publishing support function 30 7.5 Evaluating robustness under diverse data size 33 7.6 Inference through equilibrium points 33 Chapter 8 Conclusion 34 8.1 Conclusion 34석