랭킹 라벨을 정확히 예측하기 위한 유전 알고리즘의 LambdaMART 포레스트에 대한 적용

학위논문 (석사)-- 서울대학교 대학원 공과대학 컴퓨터공학부, 2017. 8. Srinivasa Rao Satti.In this thesis, principles of genetic algorithm (GA) will be applied to forests of LambdaMART to get more accurate ranking results. Ranking problem is considered one kind of prediction function problems, and various solutions were proposed for the ranking problem. Applying machine learning techniques has improved ranking quality of algorithm. One of the techniques is ensemble of decision tree learning where each tree is trained one by one and these trees are used to predict the result with the given input values. LambdaMART is a fusion of LambdaRank and MART (Multiple Additive Regression Trees), where gradients of scores are calculated by LambdaRank and multiple trees are generated and trained with predefined steps in MART. LambdaMART is also main contributor for the winner of ``Yahoo! Learning to Rank Challenge (2010)" though the challenge reports that ranking solution performance has reached saturation point. However, LambdaMART might have problems about overfitting to training data, which means it could not predict outcome precisely on other unobserved data after being trained with data. In addition, genetic algorithm can provide greater searching ability for solution space though the ability depends on designing core operations such as crossover, mutation, and so on. Combining this search ability with LambdaMART could enhance solution's quality and reduce some chance of overfitting to training data. Each LambdaMART forest will become a chromosome in this scheme, and multiple forests will be operands of genetic operations. This scheme shows higher accuracy measure value than original LambdaMART and total training time per forest has also been saved.Chapter 1 Introduction 1 Chapter 2 Background 3 2.1 Information Retrieval: Ranking 3 2.1.1 Ranking Problem 3 2.1.2 Ranking Measures 4 2.2 Classication and Regression Trees 7 2.3 Genetic Algorithm (GA) 7 2.3.1 Selection 8 2.3.2 Crossover 9 2.3.3 Mutation 10 2.3.4 Replacement 10 Chapter 3 Related Work 11 3.1 RankNet 11 3.2 LambdaRank 13 3.3 MART (Multiple Additive Regression Tree) 14 3.4 LambdaMART 15 Chapter 4 LambdaMART with GA 17 4.1 Overview 17 4.2 Genetic Operations 18 4.2.1 Selection 19 4.2.2 Crossover 19 4.2.3 Mutation 20 4.2.4 Replacement 21 Chapter 5 Experimental Results 22 5.1 System Settings and Datasets 22 5.2 Implementation 23 5.3 Results 23 Chapter 6 Conclusion 31 Bibliography 32 요약 35 Acknowledgements 36Maste

Vector for Preparation of Transformed Plant withRemoved Selectable Marker Gene and Preparation Methodof The Plant

본 발명은 식물체 형질전환용 벡터에 있어서, 형질전환을 요하는 대상 식물체에서 발현이 가능한 선발표지 및 역선발표지를 함께 포함함을 특징으로 하는 식물체 형질전환용 벡터를 제공한다. 이러한 구조의 벡터를 이용하여 식물체를 동시형질전환하는 경우 동시형질전환체 선발 후에 들여야 하는 PCR 검정법의 수고를 획기적으로 줄일 수 있고, 뿐만 아니라 검증을 위해 다수의 식물체를 키워야만 하는 수고와 재배를 위한 물리적 공간을 줄일 수 있다

Involvement of Hepatic Innate Immunity in Alcoholic Liver Disease

Excessive alcohol consumption is one of the critical causative factors leading to alcoholic liver disease (ALD). ALD is characterized by a wide spectrum of liver damage, ranging from simple uncomplicated liver steatosis (fatty liver) to steatohepatitis and liver fibrosis/cirrhosis. It has been believed that the obvious underlying cause for ALD is due to hepatocyte death induced by alcohol itself. However, recent sparkling studies have shown that diverse immune responses contribute to ALD because liver is enriched with numerous immune cells. Especially, a line of evidence has suggested that innate immune cells such as Kupffer cells and natural killer (NK)/NKT cells are significantly involved in the pathogenesis of ALD via production of pro-inflammatory cytokines and other mediators. Indeed, more interestingly, hepatic stellate cells (HSCs), known as a major cell inducing liver steatosis and fibrosis, can be killed by liver NK cells, which could be suppressed by chronic alcohol consumption. In this review, with the view of liver as predominant innate immune organ, we describe the pathogenesis of ALD in which what roles of innate immune cells are and how they are interacting with HSCs.