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El. knyga: Small Business Management: Theory and Practice

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This textbook familiarises students with the theory and practice of small business management and challenges assumptions that may be held about the way small business management can or should adopt the management practices of larger firms.







For students interested in establishing and managing their own small firm, this book helps them to focus their thinking on the realities of life as a small business owner-manager both its challenges and its rewards.







For postgraduate students that are keen to make a difference, this text enables them to understand how they might consult to small firms and assist owner-managers to establish and grow their ventures.







In addition to students, this book is also useful to small business owner-managers as a general guide on how they might better manage their operations. Managers in large corporations and financial institutions who deal with small businesses as clients or suppliers, and professionals such as accountants, lawyers and consultants who provide advice and other services to small businesses will also find the book of interest.
I Classification 1(66)
1 Learning to Classify
3(18)
1.1 Classification: The Big Ideas
4(3)
1.1.1 The Error Rate and Other Summaries of Performance
4(1)
1.1.2 More Detailed Evaluation
5(1)
1.1.3 Overfitting and Cross-Validation
6(1)
1.2 Classifying with Nearest Neighbors
7(3)
1.2.1 Practical Considerations for Nearest Neighbors
8(2)
1.3 Naive Bayes
10(6)
1.3.1 Cross-Validation to Choose a Model
13(2)
1.3.2 Missing Data
15(1)
1.4 You Should
16(5)
1.4.1 Remember These Terms
16(1)
1.4.2 Remember These Facts
16(1)
1.4.3 Remember These Procedures
17(1)
1.4.4 Be Able to
17(4)
2 SVMs and Random Forests
21(28)
2.1 The Support Vector Machine
21(13)
2.1.1 The Hinge Loss
22(2)
2.1.2 Regularization
24(1)
2.1.3 Finding a Classifier with Stochastic Gradient Descent
25(2)
2.1.4 Searching for A
27(2)
2.1.5 Summary: Training with Stochastic Gradient Descent
29(1)
2.1.6 Example: Adult Income with an SVM
30(3)
2.1.7 Multiclass Classification with SVMs
33(1)
2.2 Classifying with Random Forests
34(10)
2.2.1 Building a Decision Tree
35(3)
2.2.2 Choosing a Split with Information Gain
38(3)
2.2.3 Forests
41(1)
2.2.4 Building and Evaluating a Decision Forest
41(1)
2.2.5 Classifying Data Items with a Decision Forest
42(2)
2.3 You Should
44(5)
2.3.1 Remember These Terms
44(1)
2.3.2 Remember These Facts
44(1)
2.3.3 Use These Procedures
45(1)
2.3.4 Be Able to
45(4)
3 A Little Learning Theory
49(18)
3.1 Held-Out Loss Predicts Test Loss
49(4)
3.1.1 Sample Means and Expectations
50(2)
3.1.2 Using Chebyshev's Inequality
52(1)
3.1.3 A Generalization Bound
52(1)
3.2 Test and Training Error for a Classifier from a Finite Family
53(4)
3.2.1 Hoeffding's Inequality
54(1)
3.2.2 Test from Training for a Finite Family of Predictors
55(1)
3.2.3 Number of Examples Required
56(1)
3.3 An Infinite Collection of Predictors
57(7)
3.3.1 Predictors and Binary Functions
57(4)
3.3.2 Symmetrization
61(1)
3.3.3 Bounding the Generalization Error
62(2)
3.4 You Should
64(5)
3.4.1 Remember These Terms
64(1)
3.4.2 Remember These Facts
64(1)
3.4.3 Be Able to
65(2)
II High Dimensional Data 67(86)
4 High Dimensional Data
69(24)
4.1 Summaries and Simple Plots
69(8)
4.1.1 The Mean
70(1)
4.1.2 Stem Plots and Scatterplot Matrices
70(3)
4.1.3 Covariance
73(1)
4.1.4 The Covariance Matrix
74(3)
4.2 The Curse of Dimension
77(2)
4.2.1 The Curse: Data Isn't Where You Think It Is
77(1)
4.2.2 Minor Banes of Dimension
78(1)
4.3 Using Mean and Covariance to Understand High Dimensional Data
79(4)
4.3.1 Mean and Covariance Under Affine Transformations
80(1)
4.3.2 Eigenvectors and Diagonalization
81(1)
4.3.3 Diagonalizing Covariance by Rotating Blobs
82(1)
4.4 The Multivariate Normal Distribution
83(5)
4.4.1 Affine Transformations and Gaussian
84(1)
4.4.2 Plotting a 2D Gaussian: Covariance Ellipses
85(1)
4.4.3 Descriptive Statistics and Expectations
86(1)
4.4.4 More from the Curse of Dimension
87(1)
4.5 You Should
88(5)
4.5.1 Remember These Terms
88(1)
4.5.2 Remember These Facts
88(1)
4.5.3 Remember These Procedures
89(4)
5 Principal Component Analysis
93(24)
5.1 Representing Data on Principal Components
93(12)
5.1.1 Approximating Blobs
93(1)
5.1.2 Example: Transforming the Height-Weight Blob
94(2)
5.1.3 Representing Data on Principal Components
96(2)
5.1.4 The Error in a Low Dimensional Representation
98(1)
5.1.5 Extracting a Few Principal Components with NIPALS .
99(2)
5.1.6 Principal Components and Missing Values
101(2)
5.1.7 PCA as Smoothing
103(2)
5.2 Example: Representing Colors with Principal Components
105(4)
5.3 Example: Representing Faces with Principal Components
109(2)
5.4 You Should
111(6)
5.4.1 Remember These Terms
111(1)
5.4.2 Remember These Facts
111(1)
5.4.3 Remember These Procedures
111(1)
5.4.4 Be Able to
111(6)
6 Low Rank Approximations
117(22)
6.1 The Singular Value Decomposition
117(5)
6.1.1 SVD and PCA
119(1)
6.1.2 SVD and Low Rank Approximations
120(1)
6.1.3 Smoothing with the SVD
120(2)
6.2 Multidimensional Scaling
122(4)
6.2.1 Choosing Low D Points Using High D Distances
122(1)
6.2.2 Using a Low Rank Approximation to Factor
123(1)
6.2.3 Example: Mapping with Multidimensional Scaling
124(2)
6.3 Example: Text Models and Latent Semantic Analysis
126(10)
6.3.1 The Cosine Distance
127(1)
6.3.2 Smoothing Word Counts
128(2)
6.3.3 Example: Mapping NIPS Documents
130(1)
6.3.4 Obtaining the Meaning of Words
130(3)
6.3.5 Example: Mapping NIPS Words
133(1)
6.3.6 TF-IDF
134(2)
6.4 You Should
136(3)
6.4.1 Remember These Terms
136(1)
6.4.2 Remember These Facts
136(1)
6.4.3 Remember These Procedures
136(1)
6.4.4 Be Able to
136(3)
7 Canonical Correlation Analysis
139(14)
7.1 Canonical Correlation Analysis
139(3)
7.2 Example: CCA of Words and Pictures
142(2)
7.3 Example: CCA of Albedo and Shading
144(6)
7.3.1 Are Correlations Significant?
148(2)
7.4 You Should
150(5)
7.4.1 Remember These Terms
150(1)
7.4.2 Remember These Facts
150(1)
7.4.3 Remember These Procedures
150(1)
7.4.4 Be Able to
150(3)
III Clustering 153(50)
8 Clustering
155(28)
8.1 Agglomerative and Divisive Clustering
155(4)
8.1.1 Clustering and Distance
157(2)
8.2 The k-Means Algorithm and Variants
159(12)
8.2.1 How to Choose k
163(1)
8.2.2 Soft Assignment
164(2)
8.2.3 Efficient Clustering and Hierarchical k-Means
166(1)
8.2.4 k-Medoids
167(1)
8.2.5 Example: Groceries in Portugal
167(3)
8.2.6 General Comments on k-Means
170(1)
8.3 Describing Repetition with Vector Quantization
171(7)
8.3.1 Vector Quantization
172(3)
8.3.2 Example: Activity from Accelerometer Data
175(3)
8.4 You Should
178(5)
8.4.1 Remember These Terms
178(1)
8.4.2 Remember These Facts
178(1)
8.4.3 Remember These Procedures
178(5)
9 Clustering Using Probability Models
183(20)
9.1 Mixture Models and Clustering
183(5)
9.1.1 A Finite Mixture of Blobs
184(1)
9.1.2 Topics and Topic Models
185(3)
9.2 The EM Algorithm
188(10)
9.2.1 Example: Mixture of Normals: The E-step
189(2)
9.2.2 Example: Mixture of Normals: The M-step
191(1)
9.2.3 Example: Topic Model: The E-step
192(1)
9.2.4 Example: Topic Model: The M-step
193(1)
9.2.5 EM in Practice
193(5)
9.3 You Should
198(7)
9.3.1 Remember These Terms
198(1)
9.3.2 Remember These Facts
198(1)
9.3.3 Remember These Procedures
198(1)
9.3.4 Be Able to
198(5)
IV Regression 203(100)
10 Regression
205(40)
10.1 Overview
205(3)
10.1.1 Regression to Spot Trends
206(2)
10.2 Linear Regression and Least Squares
208(10)
10.2.1 Linear Regression
209(1)
10.2.2 Choosing i3
210(2)
10.2.3 Residuals
212(1)
10.2.4 R-squared
212(2)
10.2.5 Transforming Variables
214(3)
10.2.6 Can You Trust Your Regression?
217(1)
10.3 Visualizing Regressions to Find Problems
218(7)
10.3.1 Problem Data Points Have Significant Impact
219(3)
10.3.2 The Hat Matrix and Leverage
222(1)
10.3.3 Cook's Distance
223(1)
10.3.4 Standardized Residuals
224(1)
10.4 Many Explanatory Variables
225(11)
10.4.1 Functions of One Explanatory Variable
227(1)
10.4.2 Regularizing Linear Regressions
227(5)
10.4.3 Example: Weight Against Body Measurements
232(4)
10.5 You Should
236(9)
10.5.1 Remember These Terms
236(1)
10.5.2 Remember These Facts
236(1)
10.5.3 Remember These Procedures
236(1)
10.5.4 Be Able to
237(8)
11 Regression: Choosing and Managing Models
245(30)
11.1 Model Selection: Which Model Is Best?
245(8)
11.1.1 Bias and Variance
246(2)
11.1.2 Choosing a Model Using Penalties: AIC and BIC
248(2)
11.1.3 Choosing a Model Using Cross-Validation
250(1)
11.1.4 Greedy Search with Stagewise Regression
251(1)
11.1.5 What Variables Are Important?
252(1)
11.2 Robust Regression
253(5)
11.2.1 M-Estimators and Iteratively Reweighted Least Squares
254(3)
11.2.2 Scale for M-Estimators
257(1)
11.3 Generalized Linear Models
258(4)
11.3.1 Logistic Regression
258(2)
11.3.2 Multiclass Logistic Regression
260(1)
11.3.3 Regressing Count Data
261(1)
11.3.4 Deviance
262(1)
11.4 L1 Regularization and Sparse Models
262(9)
11.4.1 Dropping Variables with Ll Regularization
263(4)
11.4.2 Wide Datasets
267(3)
11.4.3 Using Sparsity Penalties with Other Models
270(1)
11.5 You Should
271(4)
11.5.1 Remember These Terms
271(1)
11.5.2 Remember These Facts
271(1)
11.5.3 Remember These Procedures
272(3)
12 Boosting
275(28)
12.1 Greedy and Stagewise Methods for Regression
276(8)
12.1.1 Example: Greedy Stagewise Linear Regression
276(3)
12.1.2 Regression Trees
279(1)
12.1.3 Greedy Stagewise Regression with Trees
279(5)
12.2 Boosting a Classifier
284(15)
12.2.1 The Loss
284(2)
12.2.2 Recipe: Stagewise Reduction of Loss
286(2)
12.2.3 Example: Boosting Decision Stumps
288(1)
12.2.4 Gradient Boost with Decision Stumps
289(1)
12.2.5 Gradient Boost with Other Predictors
290(1)
12.2.6 Example: Is a Prescriber an Opiate Prescriber?
291(2)
12.2.7 Pruning the Boosted Predictor with the Lasso
293(1)
12.2.8 Gradient Boosting Software
294(5)
12.3 You Should
299(6)
12.3.1 Remember These Definitions
299(1)
12.3.2 Remember These Terms
299(1)
12.3.3 Remember These Facts
299(1)
12.3.4 Remember These Procedures
300(1)
12.3.5 Be Able to
300(3)
V Graphical Models 303(62)
13 Hidden Markov Models
305(28)
13.1 Markov Chains
305(11)
13.1.1 Transition Probability Matrices
309(2)
13.1.2 Stationary Distributions
311(2)
13.1.3 Example: Markov Chain Models of Text
313(3)
13.2 Hidden Markov Models and Dynamic Programming
316(7)
13.2.1 Hidden Markov Models
316(1)
13.2.2 Picturing Inference with a Trellis
317(3)
13.2.3 Dynamic Programming for HMMs: Formalities
320(1)
13.2.4 Example: Simple Communication Errors
321(2)
13.3 Learning an HMM
323(6)
13.3.1 When the States Have Meaning
324(1)
13.3.2 Learning an HMM with EM
324(5)
13.4 You Should
329(4)
13.4.1 Remember These Terms
329(1)
13.4.2 Remember These Facts
330(1)
13.4.3 Be Able to
330(3)
14 Learning Sequence Models Discriminatively
333(18)
14.1 Graphical Models
333(5)
14.1.1 Inference and Graphs
334(2)
14.1.2 Graphical Models
336(1)
14.1.3 Learning in Graphical Models
337(1)
14.2 Conditional Random Field Models for Sequences
338(5)
14.2.1 MEMMs and Label Bias
339(2)
14.2.2 Conditional Random Field Models
341(1)
14.2.3 Learning a CRF Takes Care
342(1)
14.3 Discriminative Learning of CRFs
343(5)
14.3.1 Representing the Model
343(1)
14.3.2 Example: Modelling a Sequence of Digits
344(1)
14.3.3 Setting Up the Learning Problem
345(1)
14.3.4 Evaluating the Gradient
346(2)
14.4 You Should
348(3)
14.4.1 Remember These Terms
348(1)
14.4.2 Remember These Procedures
348(1)
14.4.3 Be Able to
348(3)
15 Mean Field Inference
351(14)
15.1 Useful but Intractable Models
351(7)
15.1.1 Denoising Binary Images with Boltzmann Machines
352(1)
15.1.2 A Discrete Markov Random Field
353(1)
15.1.3 Denoising and Segmenting with Discrete MRFs
354(3)
15.1.4 MAP Inference in Discrete MRFs Can Be Hard
357(1)
15.2 Variational Inference
358(3)
15.2.1 The KL Divergence
359(1)
15.2.2 The Variational Free Energy
360(1)
15.3 Example: Variational Inference for Boltzmann Machines
361(3)
15.4 You Should
364(3)
15.4.1 Remember These Terms
364(1)
15.4.2 Remember These Facts
364(1)
15.4.3 Be Able to
364(1)
VI Deep Networks 365(114)
16 Simple Neural Networks
367(32)
16.1 Units and Classification
367(5)
16.1.1 Building a Classifier out of Units: The Cost Function
368(1)
16.1.2 Building a Classifier out of Units: Strategy
369(1)
16.1.3 Building a Classifier out of Units: Training
370(2)
16.2 Example: Classifying Credit Card Accounts
372(5)
16.3 Layers and Networks
377(6)
16.3.1 Stacking Layers
377(2)
16.3.2 Jacobians and the Gradient
379(1)
16.3.3 Setting up Multiple Layers
380(1)
16.3.4 Gradients and Backpropagation
381(2)
16.4 Training Multilayer Networks
383(11)
16.4.1 Software Environments
385(1)
16.4.2 Dropout and Redundant Units
386(1)
16.4.3 Example: Credit Card Accounts Revisited
387(3)
16.4.4 Advanced Tricks: Gradient Scaling
390(4)
16.5 You Should
394(5)
16.5.1 Remember These Terms
394(1)
16.5.2 Remember These Facts
394(1)
16.5.3 Remember These Procedures
394(1)
16.5.4 Be Able to
395(4)
17 Simple Image Classifiers
399(24)
17.1 Image Classification
399(9)
17.1.1 Pattern Detection by Convolution
401(6)
17.1.2 Convolutional Layers upon Convolutional Layers
407(1)
17.2 Two Practical Image Classifiers
408(12)
17.2.1 Example: Classifying MNIST
410(2)
17.2.2 Example: Classifying CIFAR-10
412(6)
17.2.3 Quirks: Adversarial Examples
418(2)
17.3 You Should
420(3)
17.3.1 Remember These Definitions
420(1)
17.3.2 Remember These Terms
420(1)
17.3.3 Remember These Facts
420(1)
17.3.4 Remember These Procedures
420(1)
17.3.5 Be Able to
420(3)
18 Classifying Images and Detecting Objects
423(32)
18.1 Image Classification
423(15)
18.1.1 Datasets for Classifying Images of Objects
424(2)
18.1.2 Datasets for Classifying Images of Scenes
426(1)
18.1.3 Augmentation and Ensembles
427(1)
18.1.4 AlexNet
428(2)
18.1.5 VGGNet
430(2)
18.1.6 Batch Normalization
432(1)
18.1.7 Computation Graphs
433(1)
18.1.8 Inception Networks
434(2)
18.1.9 Residual Networks
436(2)
18.2 Object Detection
438(9)
18.2.1 How Object Detectors Work
438(2)
18.2.2 Selective Search
440(1)
18.2.3 R-CNN, Fast R-CNN and Faster R-CNN
441(2)
18.2.4 YOLO
443(2)
18.2.5 Evaluating Detectors
445(2)
18.3 Further Reading
447(2)
18.4 You Should
449(6)
18.4.1 Remember These Terms
449(1)
18.4.2 Remember These Facts
449(1)
18.4.3 Be Able to
450(5)
19 Small Codes for Big Signals
455(24)
19.1 Better Low Dimensional Maps
455(5)
19.1.1 Sammon Mapping
456(1)
19.1.2 T-SNE
457(3)
19.2 Maps That Make Low-D Representations
460(9)
19.2.1 Encoders, Decoders, and Autoencoders
461(1)
19.2.2 Making Data Blocks Bigger
462(3)
19.2.3 The Denoising Autoencoder
465(4)
19.3 Generating Images from Examples
469(6)
19.3.1 Variational Autoencoders
470(1)
19.3.2 Adversarial Losses: Fooling a Classifier
471(2)
19.3.3 Matching Distributions with Test Functions
473(1)
19.3.4 Matching Distributions by Looking at Distances
474(1)
19.4 You Should
475(4)
19.4.1 Remember These Terms
475(1)
19.4.2 Remember These Facts
476(1)
19.4.3 Be Able to
476(3)
Index 479(6)
Index: Useful Facts 485(2)
Index: Procedures 487(2)
Index: Worked Examples 489(2)
Index: Remember This 491
Tim Mazzarol is a Winthrop Professor within the UWA Business School where he specialises in entrepreneurship, innovation, small business management, marketing and strategy. He is a Qualified Professional Market Researcher (QPMR) with the Australian Market and Social Research Society (AMSRS). In addition, Tim is an Affiliate Professor of the Burgundy School of Business, Group ESC Dijon, Bourgogne France.  He is also the Coordinator of the UWA Co-operative Enterprise Research Unit (CERU), and Director of the Centre for Entrepreneurial Management and Innovation (CEMI), and a Director of the Commercialisation Studies Centre (CSC) Ltd.

Over the course of his academic career he has published a wide range of books, book chapters, journal articles, conference papers and industry reports. Tim's research interests focus on the globalisation of higher education, word of mouth marketing, business strategy, commercialisation and innovation management, small business, co-operative and mutual enterprises, business model design and marketing. In addition to his academic work Tim has been a shareholder and non-executive director of several small firms as well as a member of boards of non-profit organisations. He is actively involved in consulting to a range of organisations both large and small. In addition to his research and teaching within he is a sought-after speaker and industry trainer in the fields of strategic management, marketing and innovation.

















Sophie Reboud is professor of Entrepreneurship, Strategy and Management of Innovation at the Burgundy School of Business in Dijon, France and an honorary Research Fellow at the University of Western Australia. She has fifteen years of experience as a researcher and consultant in the field of management and strategy of small firms. Originally trained as an agronomist she served as a research engineer for École Nationale Supérieure des Mines de Paris for five years, and completed her PhD there. Sophie's research interests are in the strategic management of innovation and creativity. This includes firms in the food sector and low tech industries with specific focus on intellectual property and strategy in small firms. She is the holder of the Vitagora-BSB Research Chair on New Business Model in the Food Industry.
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