chapter 5 introduction to pig latin
Data Mining Classification: Alternative Techniques - Lecture Notes for Chapter 5 Introduction to Data Mining pdf
... according to distance • weight factor, w = 1/d2 © Tan,Steinbach, Kumar Introduction to Data Mining 41 Nearest Neighbor Classification… Choosing the value of k: – If k is too small, sensitive to noise ... Introduction to Data Mining 34 Instance-Based Classifiers • Store the training records • Use training records to predict the class label of unseen cases © Tan,Steinbach, Kumar Introduction to Data ... nearest neighbors to retrieve To classify an unknown record: – Compute distance to other training records – Identify k nearest neighbors – Use class labels of nearest neighbors to determine the...
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Chapter 5 introduction to valuation the time value of money
... and Skills • Be able to compute the future value of an investment made today • Be able to compute the present value of cash to be received at some future date • Be able to compute the return ... Excel icon to open a spreadsheet containing four different examples 5C-31 Work the Web Example • Many financial calculators are available online • Click on the web surfer to go to Investopedia’s ... simple interest? 5C-12 Present Values • How much I have to invest today to have some amount in the future? – FV = PV(1 + r)t – Rearrange to solve for PV = FV / (1 + r)t • When we talk about discounting,...
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Chapter 113. Introduction to Infectious Diseases: Host–Pathogen Interactions (Part 5) doc
... receptors, called selectins (CD62, ELAM-1), in response to inflammatory cytokines such as tumor necrosis factor α and interleukin The binding of these selectin molecules to specific receptors ... bartonellosis, Escherichia coli O157 infection, or cryptococcosis Blood transfusions have been linked to diseases ranging from viral hepatitis to malaria to prion disease A history of exposure to ... factors for exposure to certain types of pathogens may give important clues to diagnosis A sexual history may reveal risks for exposure to HIV and other sexually transmitted pathogens A history...
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Chapter 5 - LINQ to XML
... XmlNamespaceManager – Remembering to add nsName: before each operation on the DOM • LINQ to XML introduces the XName and XNamespace classes, implicitly convertible to and from string © Copyright ... Labs Ltd 14-18 Baruch Hirsch St Bnei Brak 51202 Israel LINQ to XML Performance Preatomize XName and XNamespace objects Prefer LINQ to XML (statically resolved) instead of XPath (runtime) Use ... Labs Ltd 14-18 Baruch Hirsch St Bnei Brak 51202 Israel Querying XML • The LINQ to XML query model relies on LINQ to Objects – XElement and friends implement IEnumerable var averages = from student...
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Real-Time Digital Signal Processing - Chapter 1: Introduction to Real-Time Digital Signal Processing
... breakpoint The command to enable a breakpoint can be given from the Toggle Breakpoint hot button on the project toolbar or by clicking the right mouse button and choosing toggle breakpoint The ... that it may be slow to respond to sudden changes in the input signal The dual-slope ADC uses an integrator connected to the input voltage and a reference voltage The integrator starts at zero ... the integrator output voltage to get back to zero is directly proportional to the input voltage This technique is very precise and can produce ADCs with high resolution Since the integrator is used...
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Real-Time Digital Signal Processing - Chapter 2: Introduction to TMS320C55x Digital Signal Processor
... data that can be loaded into memory space Assembler directives are used to control the assembly process and to enter data into the program Assembly directives can be used to initialize memory, define ... breakpoints to be set at a particular point in a program to examine the registers and the memory locations in order to evaluate the real-time results using a DSP board Emulators allow the DSP software to ... decrements by to point to the lower portion of AC0 (AC0L, located at address 0x8 of page 0) The third instruction copies the content of AC0L into the register T1 and modifies AR6 to point to AC0H again...
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Chapter 4 Introduction to Portfolio Theory
... investments in the two stocks The investor s problem is to decide how much wealth to put in asset A and how much to put in asset B Let xA denote the share of wealth invested in stock A and xB denote ... simple way to solve this problem is to substitute the restriction (7) into the function (6) and reduce the problem to a minimization over one variable To illustrate, use the restriction (7) to solve ... very risk tolerant investor may actually borrow at the risk free rate and use these funds to leverage her investment in the tangency portfolio For example, suppose the risk tolerant investor borrows...
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Chapter 15 introduction to the design of electric machinery
... similar to the stator backiron The inert region (radii from rrs to rri) mechanically transfers torque from the rotor backiron to the shaft It is often just a continuation of the rotor Stator Backiron ... radius, rrb—the rotor backiron radius, rrg—the rotor air-gap radius, rst—the stator tooth inner radius, rsb—the stator backiron inner radius, and rss—the stator shell radius A stator shell, if present, ... of canceled conductors to be zero and require the end winding conductor arrangement to be symmetric in the sense that for any end conductor count over tooth i, the end conductor count over the...
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Chapter 1 Introduction to Routing and Packet ForwardingRouting Protocols and Concepts quangkien@gmail.com.Topicsl Inside the Router Ÿ Routers are computers Ÿ Router CPU and Memory Ÿ Internetwork Operating System Ÿ Router Bootup Process Ÿ Router Ports doc
... Dynamic Routing Protocols: RIP and OSPF l RIP uses hop count Ÿ R1 to R3 Ÿ Fewer links but much slower l OSPF uses bandwidth Ÿ R1 to R2 to R3 Ÿ More routers but much faster links 31 To reach the 192.168.1.0/24 ... the following steps to connect a terminal to the console port on the router: l Connect the terminal using the RJ-45 to RJ-45 rollover cable and an RJ-45 to DB-9 or RJ-45 to DB-25 adapter l Configure ... the IOS stored? ROM Where is IOS permanently stored before it is copied into RAM? FLASH Where are the bootsystem commands stored which are used to locate the IOS? NVRAM running-config IOS (running)...
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Chapter 1 – Introduction to Computers and C++ Programming pot
... (stream extraction operator) • Used with std::cin • Waits for user to input value, then press Enter (Return) key • Stores value in variable to right of operator – Converts value to variable data type ... std::cout – “Connected” to screen –
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Data Mining: Introduction Lecture Notes for Chapter 1 Introduction to Data Mining ppt
... whether a customer is likely to be lost to a competitor – Approach: • Use detailed record of transactions with each of the past and present customers, to find attributes – How often the customer calls, ... © Tan,Steinbach, Kumar Total Articles 555 354 278 Introduction to Data Mining 19 Clustering of S&P 500 Stock Data Observe Stock Movements every day Clustering points: Stock-{UP/DOWN} Similarity ... {Bagels, … } > {Potato Chips} – Potato Chips as consequent => Can be used to determine what should be done to boost its sales – Bagels in the antecedent => Can be used to see which products...
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Data Mining: Data Lecture Notes for Chapter 2 Introduction to Data Mining potx
... Introduction to Data Mining Attribute Values Attribute values are numbers or symbols assigned to an attribute Distinction between attributes and attribute values – Same attribute can be mapped to different ... © Tan,Steinbach, Kumar Introduction to Data Mining 13 Document Data Each document becomes a `term' vector, – each term is a component (attribute) of the vector, – the value of each component is ... – duplicate data © Tan,Steinbach, Kumar Introduction to Data Mining 21 Noise Noise refers to modification of original values – Examples: distortion of a person’s voice when talking on a poor phone...
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Data Mining: Exploring Data Lecture Notes for Chapter 3 Introduction to Data Mining potx
... data to better understand its characteristics Key motivations of data exploration include – Helping to select the right tool for preprocessing or analysis – Making use of humans’ abilities to recognize ... patterns not captured by data analysis tools Related to the area of Exploratory Data Analysis (EDA) – Created by statistician John Tukey – Seminal book is Exploratory Data Analysis by Tukey – A nice ... so many points – Can sample, but want to preserve points in 14 © Tan,Steinbach, Kumar Introduction to Data Mining Visualization Techniques: Histograms Histogram – Usually shows the distribution...
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Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation Lecture Notes for Chapter 4 Introduction to Data Mining pptx
... Determine how to split the records • How to specify the attribute test condition? • How to determine the best split? – Determine when to stop splitting © Tan,Steinbach, Kumar Introduction to Data Mining ... Determine how to split the records • How to specify the attribute test condition? • How to determine the best split? – Determine when to stop splitting © Tan,Steinbach, Kumar Introduction to Data Mining ... condition? • How to determine the best split? – Determine when to stop splitting © Tan,Steinbach, Kumar Introduction to Data Mining 46 Stopping Criteria for Tree Induction Stop expanding a node...
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Data Mining Association Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 6 Introduction to Data Mining pdf
... Use efficient data structures to store the candidates or transactions – No need to match every candidate against every transaction © Tan,Steinbach, Kumar Introduction to Data Mining 11 Reducing ... Pointer to projected database of its ancestor node – Bitvector containing information about which transactions in the projected database contain the itemset © Tan,Steinbach, Kumar Introduction to ... Tan,Steinbach, Kumar Introduction to Data Mining 12 Illustrating Apriori Principle Found to be Infrequent Pruned supersets © Tan,Steinbach, Kumar Introduction to Data Mining 13 Illustrating Apriori...
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Data Mining Association Rules: Advanced Concepts and Algorithms Lecture Notes for Chapter 7 Introduction to Data Mining docx
... Introduction to Data Mining 26 Examples of Sequence Data Sequence Database Sequence Element (Transaction) Event (Item) Customer Purchase history of a given customer A set of items bought by a customer ... Introduction to Data Mining 20 Multi-level Association Rules Food Electronics Bread Computers Milk Wheat Skim White Foremost © Tan,Steinbach, Kumar 2% Desktop Kemps Introduction to Data Mining Home Laptop ... Tan,Steinbach, Kumar Introduction to Data Mining 15 Min-Apriori (Han et al) Document-term matrix: TID W1 W2 W3 W4 W5 D1 2 0 D2 0 2 D3 0 D4 0 1 D5 1 Example: W1 and W2 tends to appear together in the same...
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Data Mining Cluster Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 8 Introduction to Data Mining pot
... Original Points K-means Clusters One solution is to use many clusters Find parts of clusters, but need to put together © Tan,Steinbach, Kumar Introduction to Data Mining 43 Overcoming K-means Limitations ... is closer (or more similar) to one or more other points in the cluster than to any point not in the cluster contiguous clusters © Tan,Steinbach, Kumar Introduction to Data Mining 13 Types of Clusters: ... the proximities between points – Clustering is equivalent to breaking the graph into connected components, one for each cluster – Want to minimize the edge weight between clusters and maximize...
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Data Mining Cluster Analysis: Advanced Concepts and Algorithms Lecture Notes for Chapter 9 Introduction to Data Mining pot
... Rastogi, Shim © Tan,Steinbach, Kumar Introduction to Data Mining Experimental Results: CURE (centroid) (single link) Picture from CURE, Guha, Rastogi, Shim © Tan,Steinbach, Kumar Introduction to ... keep the connections to the most similar (nearest) neighbors of a point while breaking the connections to less similar points The nearest neighbors of a point tend to belong to the same class as ... (d) Introduction to Data Mining 13 Chameleon: Clustering Using Dynamic Modeling Adapt to the characteristics of the data set to find the natural clusters Use a dynamic model to measure the similarity...
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