標題: Titlebook: Data Mining and Knowledge Discovery via Logic-Based Methods; Theory, Algorithms, Evangelos Triantaphyllou Book 2010 Springer Science+Busin [打印本頁] 作者: 愚蠢地活 時間: 2025-3-21 18:30
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書目名稱Data Mining and Knowledge Discovery via Logic-Based Methods讀者反饋學科排名
作者: 與野獸博斗者 時間: 2025-3-21 23:03
A Revised Branch-and-Bound Approach for Inferring a Boolean Function from Examplesng examples. This algorithm is an extension of the B&B algorithm described in the previous chapter. Now the states of the search space are described by using more information and this seems to be critical in leading to good search results faster. This chapter is based on the developments first prese作者: 悶熱 時間: 2025-3-22 02:26
Some Fast Heuristics for Inferring a Boolean Function from Examplesor inferring a Boolean function in the form of a compact (i.e., with as few clauses as possible) CNF or DNF expression from two collections of disjoint examples. As was described in Chapters 2 and 3, the B&B approaches may take a long time to run (actually, they are of exponential time complexity).作者: forestry 時間: 2025-3-22 07:29 作者: Kinetic 時間: 2025-3-22 08:49
An Incremental Learning Algorithm for Inferring Boolean Functionsheoracle for classification and use that information to improve the understanding of the system under consideration. When the new example would unveil the need for an update, one had to use all the existing training examples, plus the newly classified example, to infer a new (and hopefully more accu作者: 有惡意 時間: 2025-3-22 12:52
The Reliability Issue in Data Mining: The Case of Computer-Aided Breast Cancer Diagnosisst data. This accuracy can be a general description of how well the extracted model classifies test data. Some studies split thisaccuracy rate into two rates: thefalse-positive andfalse-negative rates. This distinction might be more appropriate for most real-life applications. For instance, it is on作者: 有惡意 時間: 2025-3-22 20:43
Data Mining and Knowledge Discovery by Means of Monotone Boolean Functionsly inferred if all possible binary examples (states) in the space of the attributes are used for training. Thus, one may never be 100% certain about the validity of the inferred knowledge when the number of training examples is less than 2.. The situation is different, however, if one deals with the作者: 不能仁慈 時間: 2025-3-23 01:14
Some Application Issues of Monotone Boolean Functionsuracy make the search for this property in data and its consecutive algorithmic exploitation, to be of high potential in data mining and knowledge discovery applications. The following developments are based on the work described in [ Kovalerchuk,Vityaev, andTriantaphyllou, 1996] and [ Kovalerchuk,T作者: slipped-disk 時間: 2025-3-23 02:30 作者: overreach 時間: 2025-3-23 06:25 作者: Implicit 時間: 2025-3-23 09:53 作者: 無瑕疵 時間: 2025-3-23 16:26
A Fuzzy Logic Approach to Attribute Formalization: Analysis of Lobulation for Breast Cancer Diagnosis may be of significance. For easily quantifiable attributes (such as, age, weight, cost, etc.) this task is a rather straightforward one as it involves simple measurements and expressing the results in terms of some units. For other attributes, however, this task may not be a simple one. This is th作者: zonules 時間: 2025-3-23 18:29
Conclusionses some comprehensive concluding remarks. As was mentioned earlier, there are many approaches todata mining andknowledge discovery from data sets. Such approaches includeneural networks, closest neighbor methods, and various statistical methods. However, such approaches may have some severe limitati作者: 無表情 時間: 2025-3-23 23:40 作者: Accolade 時間: 2025-3-24 05:18
Algorithms and Computation in MathematicsThis chapter discusses a useful relationship between the CNF and DNF forms of the Boolean functions derivable from the same training data. This relationship can benefit approaches which attempt to solve large Boolean function inference problems and use either the CNF or the DNF form in representing a Boolean function.作者: Hypopnea 時間: 2025-3-24 09:26 作者: 滔滔不絕地說 時間: 2025-3-24 12:02 作者: defuse 時間: 2025-3-24 16:54
Inferring a Boolean Function from Positive and Negative ExamplesA central problem in data mining is how to analyze observations grouped into two categories and infer some key patterns that may be implied by these observations. As discussed in Chapter 1, these observations describe different states of nature of the system or phenomenon of interest to the analyst.作者: restrain 時間: 2025-3-24 22:33 作者: 敵意 時間: 2025-3-25 01:14 作者: BINGE 時間: 2025-3-25 06:12
First Case Study: Predicting Muscle Fatigue from EMG SignalsMost of the previous chapters discussed some application issues on a number of areas. This chapter discusses a case study in detail. The emphasis is on some comparative issues with other data mining techniques that do not use logic-based approaches. This chapter also provides a link to the data used in this study.作者: 高腳酒杯 時間: 2025-3-25 07:57
Konrad Buczkowski,Pawe? Dziekański of interest for the purpose of gaining a better understanding of it. This system of interest might be artificial or natural. According to the Merriam-Webster online dictionary the term . is derived from the Greek terms . (plus, with, along with, together, at the same time) and . (to cause to stand)作者: Opponent 時間: 2025-3-25 12:17 作者: chapel 時間: 2025-3-25 19:18
Robert Peter Gale MD, PhD,Anna Butturinior inferring a Boolean function in the form of a compact (i.e., with as few clauses as possible) CNF or DNF expression from two collections of disjoint examples. As was described in Chapters 2 and 3, the B&B approaches may take a long time to run (actually, they are of exponential time complexity).作者: APRON 時間: 2025-3-25 22:31 作者: 高原 時間: 2025-3-26 00:45
Algorithms and Computation in Mathematicsheoracle for classification and use that information to improve the understanding of the system under consideration. When the new example would unveil the need for an update, one had to use all the existing training examples, plus the newly classified example, to infer a new (and hopefully more accu作者: 釋放 時間: 2025-3-26 07:48
Haruka Mizuta,Takehiro Ito,Xiao Zhoust data. This accuracy can be a general description of how well the extracted model classifies test data. Some studies split thisaccuracy rate into two rates: thefalse-positive andfalse-negative rates. This distinction might be more appropriate for most real-life applications. For instance, it is on作者: 含沙射影 時間: 2025-3-26 10:10 作者: 職業(yè) 時間: 2025-3-26 14:35 作者: fallible 時間: 2025-3-26 18:51
Edge Exploration of Temporal Graphsed from a type of analysis that extracts information from coincidence [ Blaxton andWestphal, 1998]. Sometimes called., this methodology allows a data analyst to discover correlations, or co-occurrences of transactional events. In the classic example, consider the items contained in a customer’s shop作者: mucous-membrane 時間: 2025-3-27 00:14 作者: ungainly 時間: 2025-3-27 02:17 作者: 支柱 時間: 2025-3-27 08:41 作者: 做事過頭 時間: 2025-3-27 13:16
Lecture Notes in Computer Sciencees some comprehensive concluding remarks. As was mentioned earlier, there are many approaches todata mining andknowledge discovery from data sets. Such approaches includeneural networks, closest neighbor methods, and various statistical methods. However, such approaches may have some severe limitati作者: Merited 時間: 2025-3-27 15:12
A Revised Branch-and-Bound Approach for Inferring a Boolean Function from Examplesng examples. This algorithm is an extension of the B&B algorithm described in the previous chapter. Now the states of the search space are described by using more information and this seems to be critical in leading to good search results faster. This chapter is based on the developments first presented in [ Triantaphyllou, 1994].作者: quiet-sleep 時間: 2025-3-27 20:02
Some Fast Heuristics for Inferring a Boolean Function from Examplesor inferring a Boolean function in the form of a compact (i.e., with as few clauses as possible) CNF or DNF expression from two collections of disjoint examples. As was described in Chapters 2 and 3, the B&B approaches may take a long time to run (actually, they are of exponential time complexity).作者: 壁畫 時間: 2025-3-27 22:45 作者: intertwine 時間: 2025-3-28 03:56
Second Case Study: Inference of Diagnostic Rules for Breast Cancerets of malignant andbenign cases. We applied theOCAT approach, as it is embedded in the RA1 heuristic (see also Chapter 4), after the data were transformed into binary ones according to the method described in Section 2.2. The following sections describe the data and inferreddiagnostic rules in more detail.作者: epinephrine 時間: 2025-3-28 07:00
Conclusionses some comprehensive concluding remarks. As was mentioned earlier, there are many approaches todata mining andknowledge discovery from data sets. Such approaches includeneural networks, closest neighbor methods, and various statistical methods. However, such approaches may have some severe limitations for a number of reasons.作者: 敲竹杠 時間: 2025-3-28 11:43
Evangelos TriantaphyllouUsing a novel method, the monograph studies a series of interconnected key data mining and knowledge discovery problems.Provides a unique perspective into the essence of some fundamental Data Mining i作者: 改進 時間: 2025-3-28 17:34 作者: Introduction 時間: 2025-3-28 19:01
Data Mining and Knowledge Discovery via Logic-Based Methods978-1-4419-1630-3Series ISSN 1931-6828 Series E-ISSN 1931-6836 作者: Licentious 時間: 2025-3-28 23:30
Immune Therapy of Human Cancersng examples. This algorithm is an extension of the B&B algorithm described in the previous chapter. Now the states of the search space are described by using more information and this seems to be critical in leading to good search results faster. This chapter is based on the developments first presented in [ Triantaphyllou, 1994].作者: 群居男女 時間: 2025-3-29 03:11
Robert Peter Gale MD, PhD,Anna Butturinior inferring a Boolean function in the form of a compact (i.e., with as few clauses as possible) CNF or DNF expression from two collections of disjoint examples. As was described in Chapters 2 and 3, the B&B approaches may take a long time to run (actually, they are of exponential time complexity).作者: 討好美人 時間: 2025-3-29 08:59 作者: 盡忠 時間: 2025-3-29 11:34
Recent Advances of Palindromic Factorizationets of malignant andbenign cases. We applied theOCAT approach, as it is embedded in the RA1 heuristic (see also Chapter 4), after the data were transformed into binary ones according to the method described in Section 2.2. The following sections describe the data and inferreddiagnostic rules in more detail.作者: beta-cells 時間: 2025-3-29 16:31
Lecture Notes in Computer Sciencees some comprehensive concluding remarks. As was mentioned earlier, there are many approaches todata mining andknowledge discovery from data sets. Such approaches includeneural networks, closest neighbor methods, and various statistical methods. However, such approaches may have some severe limitations for a number of reasons.作者: 襲擊 時間: 2025-3-29 23:33 作者: 殺人 時間: 2025-3-30 03:33 作者: 壓艙物 時間: 2025-3-30 05:17
Data Mining and Knowledge Discovery via Logic-Based MethodsTheory, Algorithms, 作者: Essential 時間: 2025-3-30 12:00
Edge Exploration of Temporal Graphsexpressed in the form ofassociation rules. Such information may have tremendous potential on the marketing of new or existing products. This is the kind of approach used by many enterprises (such as Amazon.com for instance) to recommend new or existing products to their customers. Mining ofassociati作者: 防御 時間: 2025-3-30 14:04 作者: 大量 時間: 2025-3-30 18:47
Mining of Association Rulesexpressed in the form ofassociation rules. Such information may have tremendous potential on the marketing of new or existing products. This is the kind of approach used by many enterprises (such as Amazon.com for instance) to recommend new or existing products to their customers. Mining ofassociati作者: troponins 時間: 2025-3-30 22:33 作者: 評論性 時間: 2025-3-31 04:12 作者: Oversee 時間: 2025-3-31 07:05
An Approach to Guided Learning of Boolean Functions, a Boolean function in CNF or DNF form that satisfies the requirements of the positive and negative examples as described in Chapters 2 and 3. It is hoped at this point that the inferred Boolean function will accurately classify all remaining examples not included in the currently available positive and negative examples.作者: 使人煩燥 時間: 2025-3-31 10:29
An Incremental Learning Algorithm for Inferring Boolean Functions the need for an update, one had to use all the existing training examples, plus the newly classified example, to infer a new (and hopefully more accurate) pattern in the form of a Boolean function or other data mining model.作者: 凹槽 時間: 2025-3-31 16:15
The Reliability Issue in Data Mining: The Case of Computer-Aided Breast Cancer Diagnosiso rates: thefalse-positive andfalse-negative rates. This distinction might be more appropriate for most real-life applications. For instance, it is one thing to wrongly diagnose a benign tumor as malignant than the other way around. Related are some of the discussions in Sections 1.3.4, 4.5, and 11.6.作者: OPINE 時間: 2025-3-31 20:16
Data Mining of Text Documentssets of training examples (text documents) are assumed to be available. An approach is developed that usesindexing terms to form patterns of logical expressions (Boolean functions) that next are used to classify new text documents (which are of unknown class). This is a typical case ofsupervised “crisp” classification.
